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The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
This document discusses using artificial neural networks (ANNs) to enhance stock picking and investment strategies by incorporating earnings forecasts from financial analysts. It aims to compare different ANN models and identify the best model for forecasting stock prices and improving investment profitability. The study uses quarterly data on stock prices, indexes, analyst earnings forecasts and recommendations from 1997-2003 to train and evaluate ANN models. It finds that ANN strategies based on analyst forecasts achieved higher returns than other investment strategies over this period.
This document summarizes various techniques that have been used to predict stock market performance, including data mining, artificial neural networks, hidden Markov models, neuro-fuzzy systems, and rough set data modeling. It reviews several studies that have applied these techniques to predict movements in stock market indices. Specifically, it discusses research that used support vector machines and neural networks to predict changes in the Hang Seng Index, and that proposed a hybrid decision tree and neuro-fuzzy system to predict trends in four major international stock markets. The document concludes that while various techniques have been implemented, fusion models combining hidden Markov models, neural networks, and genetic algorithms may help control and monitor stock price behavior and fluctuations.
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
This document describes using a Long Short-Term Memory (LSTM) machine learning algorithm to predict future stock prices. It begins with an introduction to stock price prediction and the challenges involved. It then discusses the proposed system, which uses LSTM to more accurately predict stock prices compared to existing methods like sliding window algorithms. The system architecture involves downloading stock price data, preprocessing it, training an LSTM model, and visualizing the predictions. LSTM is well-suited for this task since it can learn from experiences with long time lags between important events. The document concludes the LSTM approach helps investors and analysts by providing a precise forecasting model for stock market prediction.
its a presentation on stock market analysis using Genetic algorithm with Neural networks ,based on a scientific paper
,made in Cairo university under Supervision of prof.Dr. Magda
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
The usage of Neural network s has determined a variegated area of packages in the present world. This has caused the
improvement of various fashions for economic markets and funding. This paper represents the idea the way to predict share
market fee the use of artificial Neural community with a given enter parameters of share marketplace. The proportion
marketplace is dynamic in nature approach to expect percentage fee could be very complex method by using trendy prediction
or computation method. Its predominant motive is that there is no linear relationship between market parameters and target last
price. Since there is no linear relationship between input patterns and corresponding output patterns, so use of neural network is
a desire of hobby for share market prediction.
This document discusses using artificial neural networks (ANNs) to enhance stock picking and investment strategies by incorporating earnings forecasts from financial analysts. It aims to compare different ANN models and identify the best model for forecasting stock prices and improving investment profitability. The study uses quarterly data on stock prices, indexes, analyst earnings forecasts and recommendations from 1997-2003 to train and evaluate ANN models. It finds that ANN strategies based on analyst forecasts achieved higher returns than other investment strategies over this period.
This document summarizes various techniques that have been used to predict stock market performance, including data mining, artificial neural networks, hidden Markov models, neuro-fuzzy systems, and rough set data modeling. It reviews several studies that have applied these techniques to predict movements in stock market indices. Specifically, it discusses research that used support vector machines and neural networks to predict changes in the Hang Seng Index, and that proposed a hybrid decision tree and neuro-fuzzy system to predict trends in four major international stock markets. The document concludes that while various techniques have been implemented, fusion models combining hidden Markov models, neural networks, and genetic algorithms may help control and monitor stock price behavior and fluctuations.
IMPROVED TURNOVER PREDICTION OF SHARES USING HYBRID FEATURE SELECTIONIJDKP
Predicting the total turnover of a company in the most unstable stock market and trade conditions has
always proved to be a costly affair causing rise and fall of several trades. Data mining is a well-known
sphere of Computer Science that aims at extracting meaningful information from large databases. However,
despite the existence of many algorithms for the purpose of predicting future trends, their efficiency is
questionable as their predictions suffer from a high error rate. The objective of this paper is to investigate
and rate the performance of classifiers based on the features selected by Hybrid Feature Selection. The
authorized dataset for predicting the turnover was taken fromwww.bsc.com and included the stock market
values of various companies over the past 10 years. The algorithms were investigated using the Weka tool.
The Hybrid feature selection (HFS) algorithm, was run on this dataset to extract the important and
influential features for classification. With these extracted features, the Total Turnover of the company was
predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.
This prediction mechanism was implemented to predict the turnover of a company on an everyday basis
and hence could help navigate through dubious stock markets trades. An accuracy rate of was achieved by
the above prediction process. Moreover, the importance of the stock market attributes through Incremental
Feature Selection (IFS) was established as well.
This document describes using a Long Short-Term Memory (LSTM) machine learning algorithm to predict future stock prices. It begins with an introduction to stock price prediction and the challenges involved. It then discusses the proposed system, which uses LSTM to more accurately predict stock prices compared to existing methods like sliding window algorithms. The system architecture involves downloading stock price data, preprocessing it, training an LSTM model, and visualizing the predictions. LSTM is well-suited for this task since it can learn from experiences with long time lags between important events. The document concludes the LSTM approach helps investors and analysts by providing a precise forecasting model for stock market prediction.
its a presentation on stock market analysis using Genetic algorithm with Neural networks ,based on a scientific paper
,made in Cairo university under Supervision of prof.Dr. Magda
PERFORMANCE ANALYSIS and PREDICTION of NEPAL STOCK MARKET (NEPSE) for INVESTM...Hari KC
The document presents research analyzing stock market performance and predicting stock prices of four Nepali companies using regression techniques. It finds that radial basis function regression most accurately predicts prices, with prediction errors ranging from 0.74-13.65%. Analysis of stock sentiment, moving averages, and interest rate correlations is also performed. Based on the analysis, Agricultural Development Bank Ltd. is identified as having the best indicators and lowest prediction error, making it the recommended investment priority.
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUERicha Handa
This document discusses using artificial neural networks (ANN) to predict stock market prices. It proposes a new ANN model for feature extraction and selection to more accurately predict stock exchange markets. The model involves preprocessing noisy stock data using technical indicators, extracting and selecting key features, then applying ANN and data mining techniques to accurately predict stock prices. Evaluation of the ANN model found that feature extraction and selection play an important role in the robustness and efficiency of predicting stock market movement.
Neuro-Fuzzy Model for Strategic Intellectual Property Cost ManagementEditor IJCATR
Strategic Intellectual property (IP) management requires strategic IP creation cost management. It is ideal to
be able to proactively estimate the cost of creating IP. This would facilitate the alignment of IP creation activities in order
to meet strategic management objectives. This paper proposes the use of Neuro-fuzzy model for strategic management
of IP cost management. The extraction of the variables for the model is based on the Activity Based Costing techniques.
Performance analysis and prediction of stock market for investment decision u...Hari KC
1) The document presents research on using regression techniques to analyze and forecast future stock prices of companies listed on the Nepalese stock exchange NEPSE.
2) The researcher aims to allow investors to make investment decisions with less risk by predicting stock market movements and stability.
3) Regression analysis and support vector machines will be used to fit linear and nonlinear models to stock price data and news sentiment to forecast prices.
Stock Market Prediction and Investment Portfolio Selection Using Computationa...iosrjce
This document discusses using computational approaches for stock market prediction and investment portfolio selection. It reviews literature on various techniques used, including linear programming, goal programming, data mining, and soft computing strategies. Soft computing approaches like neural networks, fuzzy logic, and genetic algorithms are highlighted as useful tools for analyzing the stock market to predict stock prices and guide investors. Key factors that impact the stock market are also examined, such as technical indicators, financial ratios, economic policies, and political environment. The objective is to study existing methods and help investors select profitable scripts to add to their portfolios.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
IRJET- Stock Market Forecasting Techniques: A SurveyIRJET Journal
This document surveys various techniques for stock market forecasting, including traditional and recent methods using machine learning and artificial intelligence. It discusses techniques like artificial neural networks, hidden Markov models, support vector regression, and deep learning. It also reviews several research papers that have applied methods like ARIMA models, improved Levenberg-Marquardt training for neural networks, feedforward neural networks for the Stock Exchange of Thailand index, improved multiple linear regression in an Android app, support vector regression with windowing operators on the Dhaka Stock Exchange, hidden Markov models compared to neural networks and support vector machines, a hybrid support vector regression and filtering model, and using J48 decision trees and random forests with preprocessing.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
IRJET- Stock Price Prediction using Long Short Term MemoryIRJET Journal
This document proposes using a Long Short Term Memory (LSTM) recurrent neural network for online stock price prediction. It discusses the limitations of existing batch processing methods and other neural networks in capturing the correlated temporal dependencies in stock price data. The document describes preprocessing stock data and training an LSTM model to predict the end-of-day price. It compares the proposed LSTM approach to other methods and evaluates the predictions against actual prices using metrics like root mean squared error.
IRJET- Stock Price Prediction using combination of LSTM Neural Networks, ARIM...IRJET Journal
This document proposes a method to predict stock prices using a combination of long short-term memory neural networks, autoregressive integrated moving average time series modeling, and sentiment analysis. These three techniques are combined in an ensemble learning approach using a feedforward neural network to make final predictions. By combining deep learning, time series analysis, and natural language processing, the system aims to generate more accurate stock price forecasts.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
IRJET- Data Visualization and Stock Market and PredictionIRJET Journal
This document discusses using machine learning techniques like LSTM neural networks to predict stock market prices. It summarizes the following:
1) Traditional stock prediction methods like fundamental and statistical analysis have limitations, while machine learning approaches like LSTM networks can better capture long-term temporal dependencies in stock price data.
2) The document outlines collecting stock price history, preprocessing the data, and using an LSTM model in Keras to predict future stock prices based on historical closing prices and trading volumes.
3) The model was able to accurately predict stock prices on unseen Facebook data, demonstrating the robustness of the machine learning approach over traditional methods for this challenging problem.
Turnover Prediction of Shares Using Data Mining Techniques : A Case Study csandit
Predicting the Total turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task at hand. Data mining is a
well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of
predicting future trends, their efficiency is questionable as their predictions suffer from a high
error rate. The objective of this paper is to investigate various existing classification algorithms
to predict the turnover of different companies based on the Stock price. The authorized datasetfor predicting the turnover was taken from www.bsc.com and included the stock market valuesof various companies over the past 10 years. The algorithms were investigated using the ‘R’
tool. The feature selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the Total Turnover of
the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious
stock markets trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of the stock market attributes was established as well.
IRJET- Machine Learning: Introduction, Algorithms and ImplementationIRJET Journal
This document provides an overview of machine learning, including:
1. It discusses how machine learning has evolved from a niche field to an independent research discipline that has developed many algorithms routinely used for tasks like text analysis and pattern recognition.
2. It explains some key machine learning algorithms and compares the performance of three popular algorithms (training time, prediction time, accuracy) on a sentiment analysis dataset.
3. It describes how machine learning combines computer science and statistics by having computers learn from examples or experiences to improve performance, rather than being explicitly programmed.
Ridge and random forest regression techniques were used to develop a mathematical model to calculate the cross-validation score and predict stock price volatility of companies. The model aims to determine if a firm's stock prices remain fluctuating or stable and analyze trends in price changes over time. Researchers found directional stock price movements were over 90% predictable given past opening and closing prices, though the magnitude of price changes could not be determined with certainty.
Stock Ranking - A Neural Networks Approachriturajvasant
The document discusses using neural networks to perform stock ranking. It begins with defining stock ranking and neural networks. It then discusses why neural networks are well-suited for stock ranking due to their ability to model nonlinear relationships. The methodology section outlines the steps: variable selection, data collection and preprocessing, neural network selection including training and validation. Results show neural networks outperform traditional linear models for stock ranking.
The project involved studying some of the popular filters and prediction algorithms used for stock market analysis. Based on that Moving Average Filter, Adaptive Kalman Filter, Multiple Linear Regression Filter, Bollinger Bands, and Chaikin Oscillator were developed and implemented in MATLAB. For carrying out the analysis, daily stock market data of 10 popular companies, over a period of 1 year was used. The overall project developed can be used as a complete package to carry out accurate and efficient stock market analysis and trend study.
A travel, politics, and culture website called Nomad provides information across multiple topics including travel, politics, and creative writing. The website covers a wide range of subjects from different fields in concise articles and stories. Nomad aims to inform readers about the world through various lenses.
PERFORMANCE ANALYSIS and PREDICTION of NEPAL STOCK MARKET (NEPSE) for INVESTM...Hari KC
The document presents research analyzing stock market performance and predicting stock prices of four Nepali companies using regression techniques. It finds that radial basis function regression most accurately predicts prices, with prediction errors ranging from 0.74-13.65%. Analysis of stock sentiment, moving averages, and interest rate correlations is also performed. Based on the analysis, Agricultural Development Bank Ltd. is identified as having the best indicators and lowest prediction error, making it the recommended investment priority.
STOCK MARKET PRREDICTION WITH FEATURE EXTRACTION USING NEURAL NETWORK TEHNIQUERicha Handa
This document discusses using artificial neural networks (ANN) to predict stock market prices. It proposes a new ANN model for feature extraction and selection to more accurately predict stock exchange markets. The model involves preprocessing noisy stock data using technical indicators, extracting and selecting key features, then applying ANN and data mining techniques to accurately predict stock prices. Evaluation of the ANN model found that feature extraction and selection play an important role in the robustness and efficiency of predicting stock market movement.
Neuro-Fuzzy Model for Strategic Intellectual Property Cost ManagementEditor IJCATR
Strategic Intellectual property (IP) management requires strategic IP creation cost management. It is ideal to
be able to proactively estimate the cost of creating IP. This would facilitate the alignment of IP creation activities in order
to meet strategic management objectives. This paper proposes the use of Neuro-fuzzy model for strategic management
of IP cost management. The extraction of the variables for the model is based on the Activity Based Costing techniques.
Performance analysis and prediction of stock market for investment decision u...Hari KC
1) The document presents research on using regression techniques to analyze and forecast future stock prices of companies listed on the Nepalese stock exchange NEPSE.
2) The researcher aims to allow investors to make investment decisions with less risk by predicting stock market movements and stability.
3) Regression analysis and support vector machines will be used to fit linear and nonlinear models to stock price data and news sentiment to forecast prices.
Stock Market Prediction and Investment Portfolio Selection Using Computationa...iosrjce
This document discusses using computational approaches for stock market prediction and investment portfolio selection. It reviews literature on various techniques used, including linear programming, goal programming, data mining, and soft computing strategies. Soft computing approaches like neural networks, fuzzy logic, and genetic algorithms are highlighted as useful tools for analyzing the stock market to predict stock prices and guide investors. Key factors that impact the stock market are also examined, such as technical indicators, financial ratios, economic policies, and political environment. The objective is to study existing methods and help investors select profitable scripts to add to their portfolios.
Comparison of Cost Estimation Methods using Hybrid Artificial Intelligence on...IJERA Editor
Cost estimating at schematic design stage as the basis of project evaluation, engineering design, and cost
management, plays an important role in project decision under a limited definition of scope and constraints in
available information and time, and the presence of uncertainties. The purpose of this study is to compare the
performance of cost estimation models of two different hybrid artificial intelligence approaches: regression
analysis-adaptive neuro fuzzy inference system (RANFIS) and case based reasoning-genetic algorithm (CBRGA)
techniques. The models were developed based on the same 50 low-cost apartment project datasets in
Indonesia. Tested on another five testing data, the models were proven to perform very well in term of accuracy.
A CBR-GA model was found to be the best performer but suffered from disadvantage of needing 15 cost drivers
if compared to only 4 cost drivers required by RANFIS for on-par performance.
IRJET- Stock Market Forecasting Techniques: A SurveyIRJET Journal
This document surveys various techniques for stock market forecasting, including traditional and recent methods using machine learning and artificial intelligence. It discusses techniques like artificial neural networks, hidden Markov models, support vector regression, and deep learning. It also reviews several research papers that have applied methods like ARIMA models, improved Levenberg-Marquardt training for neural networks, feedforward neural networks for the Stock Exchange of Thailand index, improved multiple linear regression in an Android app, support vector regression with windowing operators on the Dhaka Stock Exchange, hidden Markov models compared to neural networks and support vector machines, a hybrid support vector regression and filtering model, and using J48 decision trees and random forests with preprocessing.
SCCAI- A Student Career Counselling Artificial Intelligencevivatechijri
As education is growing day by day, the competition has prompted a need for the student to
understand more about the educational field. Many times the counselor isn’t available all the time and
sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of
misconception of that field. This creates a problem for the student to decide a proper educational trajectory and
guidance is not always useful. The proposed paper will overcome all these problem using machine learning
algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used
here. There are 3 major problems that come across our path and they are solved using Random forest, Linear
regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of
location by segregating the college’s location vice, then Random Forest provides the list of colleges by using
stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’
data. Rather than this, the proposed system also provides information regarding all fields of education helping
students to understand and know about their field of interest better. The following idea is a total fresh idea with
no existing projects of similar kind. This project will help students guide them throughout.
IRJET- Stock Price Prediction using Long Short Term MemoryIRJET Journal
This document proposes using a Long Short Term Memory (LSTM) recurrent neural network for online stock price prediction. It discusses the limitations of existing batch processing methods and other neural networks in capturing the correlated temporal dependencies in stock price data. The document describes preprocessing stock data and training an LSTM model to predict the end-of-day price. It compares the proposed LSTM approach to other methods and evaluates the predictions against actual prices using metrics like root mean squared error.
IRJET- Stock Price Prediction using combination of LSTM Neural Networks, ARIM...IRJET Journal
This document proposes a method to predict stock prices using a combination of long short-term memory neural networks, autoregressive integrated moving average time series modeling, and sentiment analysis. These three techniques are combined in an ensemble learning approach using a feedforward neural network to make final predictions. By combining deep learning, time series analysis, and natural language processing, the system aims to generate more accurate stock price forecasts.
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
The document discusses using genetic algorithms for financial forecasting. It begins with an abstract that notes genetic algorithms have been used extensively in various domains including finance to generate profitable trading rules. The document then provides background on genetic algorithms and their basic functions like selection, crossover and mutation. It explains how genetic algorithms can be used to develop a model for financial forecasting by evaluating trading rules based on historical data to determine which rules would have yielded the highest returns.
IRJET- Data Visualization and Stock Market and PredictionIRJET Journal
This document discusses using machine learning techniques like LSTM neural networks to predict stock market prices. It summarizes the following:
1) Traditional stock prediction methods like fundamental and statistical analysis have limitations, while machine learning approaches like LSTM networks can better capture long-term temporal dependencies in stock price data.
2) The document outlines collecting stock price history, preprocessing the data, and using an LSTM model in Keras to predict future stock prices based on historical closing prices and trading volumes.
3) The model was able to accurately predict stock prices on unseen Facebook data, demonstrating the robustness of the machine learning approach over traditional methods for this challenging problem.
Turnover Prediction of Shares Using Data Mining Techniques : A Case Study csandit
Predicting the Total turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task at hand. Data mining is a
well-known sphere of Computer Science that aims at extracting meaningful information from large databases. However, despite the existence of many algorithms for the purpose of
predicting future trends, their efficiency is questionable as their predictions suffer from a high
error rate. The objective of this paper is to investigate various existing classification algorithms
to predict the turnover of different companies based on the Stock price. The authorized datasetfor predicting the turnover was taken from www.bsc.com and included the stock market valuesof various companies over the past 10 years. The algorithms were investigated using the ‘R’
tool. The feature selection algorithm, Boruta, was run on this dataset to extract the important
and influential features for classification. With these extracted features, the Total Turnover of
the company was predicted using various algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression. This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious
stock markets trades. An accuracy rate of 95% was achieved by the above prediction process.
Moreover, the importance of the stock market attributes was established as well.
IRJET- Machine Learning: Introduction, Algorithms and ImplementationIRJET Journal
This document provides an overview of machine learning, including:
1. It discusses how machine learning has evolved from a niche field to an independent research discipline that has developed many algorithms routinely used for tasks like text analysis and pattern recognition.
2. It explains some key machine learning algorithms and compares the performance of three popular algorithms (training time, prediction time, accuracy) on a sentiment analysis dataset.
3. It describes how machine learning combines computer science and statistics by having computers learn from examples or experiences to improve performance, rather than being explicitly programmed.
Ridge and random forest regression techniques were used to develop a mathematical model to calculate the cross-validation score and predict stock price volatility of companies. The model aims to determine if a firm's stock prices remain fluctuating or stable and analyze trends in price changes over time. Researchers found directional stock price movements were over 90% predictable given past opening and closing prices, though the magnitude of price changes could not be determined with certainty.
Stock Ranking - A Neural Networks Approachriturajvasant
The document discusses using neural networks to perform stock ranking. It begins with defining stock ranking and neural networks. It then discusses why neural networks are well-suited for stock ranking due to their ability to model nonlinear relationships. The methodology section outlines the steps: variable selection, data collection and preprocessing, neural network selection including training and validation. Results show neural networks outperform traditional linear models for stock ranking.
The project involved studying some of the popular filters and prediction algorithms used for stock market analysis. Based on that Moving Average Filter, Adaptive Kalman Filter, Multiple Linear Regression Filter, Bollinger Bands, and Chaikin Oscillator were developed and implemented in MATLAB. For carrying out the analysis, daily stock market data of 10 popular companies, over a period of 1 year was used. The overall project developed can be used as a complete package to carry out accurate and efficient stock market analysis and trend study.
A travel, politics, and culture website called Nomad provides information across multiple topics including travel, politics, and creative writing. The website covers a wide range of subjects from different fields in concise articles and stories. Nomad aims to inform readers about the world through various lenses.
This document provides an overview of social enterprises and their potential to address social issues in Eusébio, Brazil. It begins with an analysis of key challenges facing the community, such as income inequality, lack of skilled labor, and public health issues. It then discusses the benefits of social enterprises and provides case studies of successful social enterprises addressing issues like sanitation, solar energy access, and textile care. The document poses research questions on structuring social enterprises and evaluating their impact. It proposes two social enterprise models for Eusébio - a community garden/nursery retail hub and an accelerator/incubator. The document concludes by discussing opportunities for policy support and partnership building to catalyze social innovation in Eusé
Geoff Lindsay received certification on October 6, 2016. His candidate ID is UQ7K and his certificate ID is CVXX4. The document provides certification details for an individual named Geoff Lindsay.
EDIT_Q4 DM Letter - The Combo_rev3_11 14 13Jordan Walsh
The document is a letter from the CEO of Universal Technical Institute encouraging the student to enroll in their technician training program. It summarizes that (1) employment in automotive, diesel, motorcycle, and collision repair industries is expected to grow significantly in the coming years, (2) UTI partners with top manufacturers to provide hands-on training and quick graduation with in-demand skills, and (3) over $12 million in scholarships are available to make training more affordable and UTI has resources to help students with financing, relocation, employment, and completing the program.
Michelle Mayes is seeking a position in business management or human resources. She has over 9 years of experience in customer service management and tax preparation. Currently, she is the lead tax preparer at Jackson Hewitt Tax Service where she prepares taxes, handles disputes with the IRS, and trains new employees. Previously, she was the store manager at Dollar General where she oversaw daily operations, interviewed and trained staff, and reviewed financial reports. Mayes has exceptional leadership skills and the ability to perform well under pressure. She is working towards an Associate in Applied Science degree in Business Management and Human Resources from York Technical College.
The article summarizes the experience of Caddle Inc., a digital couponing app, on the CBC reality show Dragons' Den. Caddle was seeking $125,000 for a 15% stake in the company. They received multiple offers from dragons and ultimately made a deal with four out of five dragons, who invested $125,000 for a 28% stake. Caddle's CEO and CMO felt the appearance on the show provided great exposure and marketing value, and will help attract new clients and investment. They believe the experience has already been a major victory for their company.
Noni Keys is seeking a position that utilizes her experience in mental health, criminal justice, and law enforcement. She has a diverse background, including positions as an overnight specialist for mentally disabled clients, a family living assistant, a phlebotomist, and a juvenile detention officer. Keys has strong computer, organizational, and time management skills and is able to work well independently and as part of a team.
This certificate recognizes Frederic Lafleur Parfaite for achievement in creating the most awesome Spark site to date. It was presented by the Collaboration & Communities team in 2016 for outstanding work on a Spark site.
This transcript is for Samantha Swanson and shows her coursework and grades from Fall 2013 through Spring 2016 at Augsburg College. Over this period she took courses in subjects like physics, mathematics, chemistry, computer science, and accounting. Her cumulative GPA after Spring 2016 was 3.250 out of 4.0 based on 81 total credits completed. Her transcript indicates steady progress through her undergraduate degree program with a mix of major-related and general education courses each semester.
1) The document analyzes trends in the stock market using machine learning techniques like XGBoost, random forests and regression.
2) It collects stock market data using Python APIs and performs feature engineering and sentiment analysis on tweets to predict stock price patterns.
3) It finds that XGBoost achieves good accuracy without overfitting due to feature engineering and evaluation of parameter settings like term lengths.
IRJET- Prediction of Stock Market using Machine Learning AlgorithmsIRJET Journal
The document discusses predicting stock market prices using machine learning algorithms. It reviews past research applying algorithms like KNN, neural networks, ARIMA and random forest to stock price prediction. The paper aims to compare the performance of supervised learning algorithms like logistic regression, KNN and random forest on stock market datasets to determine the most accurate for predicting future prices. It reviews literature on the topic and discusses the methodology and algorithms that will be used to make predictions on datasets from five companies.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, isualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform “intelligent" tasks similar to those performed by the human brain. Artificial Neural Networks are being counted as the wave of the future in computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks. Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier structure of the neural network, namely input layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast accuracy.
IMPROVING CNN-BASED STOCK TRADING BY CONSIDERING DATA HETEROGENEITY AND BURST IJCI JOURNAL
In recent years, there have been quite a few attempts to apply intelligent techniques to financial trading, i.e., constructing automatic and intelligent trading framework based on historical stock price. Due to the unpredictable, uncertainty and volatile nature of financial market, researchers have also resorted to deep learning to construct the intelligent trading framework. In this paper, we propose to use CNN as the core functionality of such framework, because it is able to learn the spatial dependency (i.e., between rows and columns) of the input data. However, different with existing deep learning-based trading frameworks, we develop novel normalization process to prepare the stock data. In particular, we first empirically observe that the stock data is intrinsically heterogeneous and bursty, and then validate the heterogeneity and burst nature of stock data from a statistical perspective. Next, we design the data normalization method in a way such that the data heterogeneity is preserved and bursty events are suppressed. We verify out developed CNN-based trading framework plus our new normalization method on 29 stocks. Experiment results show that our approach can outperform other comparing approaches.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
A novel hybrid deep learning model for price prediction IJECEIAES
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
This document presents research on predicting stock market trends in Tehran, Iran using machine learning and deep learning algorithms. Ten years of historical data from four stock market groups were analyzed using nine machine learning models (Decision Tree, Random Forest, Adaboost, XGBoost, SVC, Naive Bayes, KNN, Logistic Regression, ANN) and two deep learning models (RNN, LSTM). Ten technical indicators were used as input values in both continuous and binary formats to evaluate the models. The results showed that RNN and LSTM performed best on continuous data, outperforming other models, while on binary data they still performed best but with less difference between models due to improved performance.
Visualizing and Forecasting Stocks Using Machine LearningIRJET Journal
This document discusses using machine learning techniques like regression and LSTM models to predict stock market returns. It first provides background on the challenges of predicting the stock market due to its unpredictable nature. It then describes obtaining stock price data from Yahoo Finance to use as the dataset. The document outlines using regression analysis to build a relationship between stock prices and time and using LSTM due to its ability to learn from sequence data. It then reviews related work applying machine learning like neural networks and genetic algorithms to optimize stock prediction. The methodology section provides more detail on preprocessing the dataset and using regression and LSTM models to make predictions and compare results.
A Comparison of Stock Trend Prediction Using Accuracy Driven Neural Network V...idescitation
In the recent scenario, nevertheless to say, modern
finance is facing many hurdles to find effective ways to gather
information about stock market data at one shot. At the same
time it is inevitable for both individuals & institutions to
visualize, summarize & enhance their knowledge about the
market behavior for making wise decisions. This paper surveys
recent literature in the domain of neural network variants to
forecast the stock market trends. Classification is made in
terms of dependant variables, data preprocessing techniques
used, network structure, performance analysis and other
useful modeling information. Through the surveyed papers it
is shown that the neural network variants are widely accepted
to study and evaluate stock market behavior compared to
standalone neural network.
Stock Market Prediction using Long Short-Term MemoryIRJET Journal
This document discusses using a Long Short-Term Memory (LSTM) model to predict stock market prices. It begins by introducing the problem of predicting stock markets and how machine learning techniques like LSTM can help. It then discusses collecting stock price data and designing an LSTM model in Python using Keras and other libraries. The model is trained on historical stock price data to identify patterns and predict future prices. The document suggests LSTM models are well-suited for this due to their ability to use past data in predictions. It evaluates the model's predictions against actual prices to determine accuracy.
The document summarizes research on machine learning techniques for stock market prediction. It classifies techniques into three categories: time series analysis, neural networks, and hybrid techniques. Neural networks are identified as generally performing best, especially when combined with data preprocessing methods. The document implements and compares techniques including layered recurrent neural networks (LRNN), which performed better than feedforward neural networks and wavelet neural network (Wsmpca-NN) at predicting stock prices of companies.
Analysis of Nifty 50 index stock market trends using hybrid machine learning ...IJECEIAES
Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifier (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1.
This paper describes an architecture for predicting the price of cryptocurrencies for the next seven days
using the Adaptive Network Based Fuzzy Inference System (ANFIS). Historical data of cryptocurrencies
and indexes that are considered are Bitcoin (BTC), Ethereum (ETH), Bitcoin Dominance (BTC.D), and
Ethereum Dominance (ETH.D) in
adaily timeframe. The methods used to teach the data are hybrid and
backpropagation algorithms, as well as grid partition, subtractive clustering
, and Fuzzy C-means
clustering (FCM) algorithms, which are used in data clustering. The architectural performance designed
in this paper has been compared with different inputs and neural network models in terms of statistical
evaluation criteria. Finally, the proposed method can predict the price of digital currencies in a short time
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Impact and Implications of Operations Research in Stock Marketinventionjournals
The motivation of this article is to advocate the administrative routine of settling on choices construct in light of instinct, as well as instinct combined with quantitative investigation. Operations Research (OR) is one of the main administrative choice science instruments utilized by benefit and charitable, for example, stock market. Gauging stock return is an important financial subject that has attracted researchers' consideration for a long time. It includes a supposition that basic data openly accessible in the past has some prescient connections to the future stock returns. This review tries to help the financial specialists in the stock market to choose the better planning for purchasing or offering stocks based on the information extricated from the chronicled costs of such stocks. The choice taken will be founded on choice tree classifier which is one of the Operations Research techniques.
Impact and Implications of Operations Research in Stock Marketinventionjournals
The motivation of this article is to advocate the administrative routine of settling on choices construct in light of instinct, as well as instinct combined with quantitative investigation. Operations Research (OR) is one of the main administrative choice science instruments utilized by benefit and charitable, for example, stock market. Gauging stock return is an important financial subject that has attracted researchers' consideration for a long time. It includes a supposition that basic data openly accessible in the past has some prescient connections to the future stock returns. This review tries to help the financial specialists in the stock market to choose the better planning for purchasing or offering stocks based on the information extricated from the chronicled costs of such stocks. The choice taken will be founded on choice tree classifier which is one of the Operations Research techniques.
This document summarizes a report on analyzing a stock prediction model using neural networks. The report presents a model that predicts stock prices by extracting stock data, dividing it into training and validation sets, and feeding it into a neural network. Experimental results showed the model could accurately predict stock prices after training on 90% of the data, but predictions on the remaining 10% of data sometimes differed from actual prices. The model allows users to choose different stock attributes or time periods for analysis and prediction.
STOCK MARKET PREDICTION AND ANALYSIS USING MACHINE LEARNING ALGORITHMSIRJET Journal
This document discusses using machine learning algorithms like LSTM and linear regression to predict stock market prices. It proposes using techniques like stacked LSTM on historical stock data to make predictions. The document outlines collecting data, preprocessing it, training models on training data and evaluating them on test data. It suggests comparing the accuracy of linear regression, stacked LSTM and other models to determine the most accurate for predicting 30 days of future stock prices. The goal is to reduce investment risk through more accurate machine-learned stock predictions.
Survey Paper on Stock Prediction Using Machine Learning AlgorithmsIRJET Journal
This document discusses various machine learning algorithms that have been used for stock market prediction, including CNN, ARIMA, LSTM, random forests, and support vector machines. It provides a literature review of past research applying these algorithms to predict stock prices using historical data. The document concludes that LSTM and ARIMA models generally provide the best predictions based on evaluating various algorithms on large datasets of historical stock market data.
Similar to International Journal of Engineering Research and Development (IJERD) (20)
A Novel Method for Prevention of Bandwidth Distributed Denial of Service AttacksIJERD Editor
Distributed Denial of Service (DDoS) Attacks became a massive threat to the Internet. Traditional
Architecture of internet is vulnerable to the attacks like DDoS. Attacker primarily acquire his army of Zombies,
then that army will be instructed by the Attacker that when to start an attack and on whom the attack should be
done. In this paper, different techniques which are used to perform DDoS Attacks, Tools that were used to
perform Attacks and Countermeasures in order to detect the attackers and eliminate the Bandwidth Distributed
Denial of Service attacks (B-DDoS) are reviewed. DDoS Attacks were done by using various Flooding
techniques which are used in DDoS attack.
The main purpose of this paper is to design an architecture which can reduce the Bandwidth
Distributed Denial of service Attack and make the victim site or server available for the normal users by
eliminating the zombie machines. Our Primary focus of this paper is to dispute how normal machines are
turning into zombies (Bots), how attack is been initiated, DDoS attack procedure and how an organization can
save their server from being a DDoS victim. In order to present this we implemented a simulated environment
with Cisco switches, Routers, Firewall, some virtual machines and some Attack tools to display a real DDoS
attack. By using Time scheduling, Resource Limiting, System log, Access Control List and some Modular
policy Framework we stopped the attack and identified the Attacker (Bot) machines
Hearing loss is one of the most common human impairments. It is estimated that by year 2015 more
than 700 million people will suffer mild deafness. Most can be helped by hearing aid devices depending on the
severity of their hearing loss. This paper describes the implementation and characterization details of a dual
channel transmitter front end (TFE) for digital hearing aid (DHA) applications that use novel micro
electromechanical- systems (MEMS) audio transducers and ultra-low power-scalable analog-to-digital
converters (ADCs), which enable a very-low form factor, energy-efficient implementation for next-generation
DHA. The contribution of the design is the implementation of the dual channel MEMS microphones and powerscalable
ADC system.
Influence of tensile behaviour of slab on the structural Behaviour of shear c...IJERD Editor
-A composite beam is composed of a steel beam and a slab connected by means of shear connectors
like studs installed on the top flange of the steel beam to form a structure behaving monolithically. This study
analyzes the effects of the tensile behavior of the slab on the structural behavior of the shear connection like slip
stiffness and maximum shear force in composite beams subjected to hogging moment. The results show that the
shear studs located in the crack-concentration zones due to large hogging moments sustain significantly smaller
shear force and slip stiffness than the other zones. Moreover, the reduction of the slip stiffness in the shear
connection appears also to be closely related to the change in the tensile strain of rebar according to the increase
of the load. Further experimental and analytical studies shall be conducted considering variables such as the
reinforcement ratio and the arrangement of shear connectors to achieve efficient design of the shear connection
in composite beams subjected to hogging moment.
Gold prospecting using Remote Sensing ‘A case study of Sudan’IJERD Editor
Gold has been extracted from northeast Africa for more than 5000 years, and this may be the first
place where the metal was extracted. The Arabian-Nubian Shield (ANS) is an exposure of Precambrian
crystalline rocks on the flanks of the Red Sea. The crystalline rocks are mostly Neoproterozoic in age. ANS
includes the nations of Israel, Jordan. Egypt, Saudi Arabia, Sudan, Eritrea, Ethiopia, Yemen, and Somalia.
Arabian Nubian Shield Consists of juvenile continental crest that formed between 900 550 Ma, when intra
oceanic arc welded together along ophiolite decorated arc. Primary Au mineralization probably developed in
association with the growth of intra oceanic arc and evolution of back arc. Multiple episodes of deformation
have obscured the primary metallogenic setting, but at least some of the deposits preserve evidence that they
originate as sea floor massive sulphide deposits.
The Red Sea Hills Region is a vast span of rugged, harsh and inhospitable sector of the Earth with
inimical moon-like terrain, nevertheless since ancient times it is famed to be an abode of gold and was a major
source of wealth for the Pharaohs of ancient Egypt. The Pharaohs old workings have been periodically
rediscovered through time. Recent endeavours by the Geological Research Authority of Sudan led to the
discovery of a score of occurrences with gold and massive sulphide mineralizations. In the nineties of the
previous century the Geological Research Authority of Sudan (GRAS) in cooperation with BRGM utilized
satellite data of Landsat TM using spectral ratio technique to map possible mineralized zones in the Red Sea
Hills of Sudan. The outcome of the study mapped a gossan type gold mineralization. Band ratio technique was
applied to Arbaat area and a signature of alteration zone was detected. The alteration zones are commonly
associated with mineralization. The alteration zones are commonly associated with mineralization. A filed check
confirmed the existence of stock work of gold bearing quartz in the alteration zone. Another type of gold
mineralization that was discovered using remote sensing is the gold associated with metachert in the Atmur
Desert.
Reducing Corrosion Rate by Welding DesignIJERD Editor
This document summarizes a study on reducing corrosion rates in steel through welding design. The researchers tested different welding groove designs (X, V, 1/2X, 1/2V) and preheating temperatures (400°C, 500°C, 600°C) on ferritic malleable iron samples. Testing found that X and V groove designs with 500°C and 600°C preheating had corrosion rates of 0.5-0.69% weight loss after 14 days, compared to 0.57-0.76% for 400°C preheating. Higher preheating reduced residual stresses which decreased corrosion. Residual stresses were 1.7 MPa for optimal X groove and 600°C
Router 1X3 – RTL Design and VerificationIJERD Editor
Routing is the process of moving a packet of data from source to destination and enables messages
to pass from one computer to another and eventually reach the target machine. A router is a networking device
that forwards data packets between computer networks. It is connected to two or more data lines from different
networks (as opposed to a network switch, which connects data lines from one single network). This paper,
mainly emphasizes upon the study of router device, it‟s top level architecture, and how various sub-modules of
router i.e. Register, FIFO, FSM and Synchronizer are synthesized, and simulated and finally connected to its top
module.
Active Power Exchange in Distributed Power-Flow Controller (DPFC) At Third Ha...IJERD Editor
This paper presents a component within the flexible ac-transmission system (FACTS) family, called
distributed power-flow controller (DPFC). The DPFC is derived from the unified power-flow controller (UPFC)
with an eliminated common dc link. The DPFC has the same control capabilities as the UPFC, which comprise
the adjustment of the line impedance, the transmission angle, and the bus voltage. The active power exchange
between the shunt and series converters, which is through the common dc link in the UPFC, is now through the
transmission lines at the third-harmonic frequency. DPFC multiple small-size single-phase converters which
reduces the cost of equipment, no voltage isolation between phases, increases redundancy and there by
reliability increases. The principle and analysis of the DPFC are presented in this paper and the corresponding
simulation results that are carried out on a scaled prototype are also shown.
Mitigation of Voltage Sag/Swell with Fuzzy Control Reduced Rating DVRIJERD Editor
Power quality has been an issue that is becoming increasingly pivotal in industrial electricity
consumers point of view in recent times. Modern industries employ Sensitive power electronic equipments,
control devices and non-linear loads as part of automated processes to increase energy efficiency and
productivity. Voltage disturbances are the most common power quality problem due to this the use of a large
numbers of sophisticated and sensitive electronic equipment in industrial systems is increased. This paper
discusses the design and simulation of dynamic voltage restorer for improvement of power quality and
reduce the harmonics distortion of sensitive loads. Power quality problem is occurring at non-standard
voltage, current and frequency. Electronic devices are very sensitive loads. In power system voltage sag,
swell, flicker and harmonics are some of the problem to the sensitive load. The compensation capability
of a DVR depends primarily on the maximum voltage injection ability and the amount of stored
energy available within the restorer. This device is connected in series with the distribution feeder at
medium voltage. A fuzzy logic control is used to produce the gate pulses for control circuit of DVR and the
circuit is simulated by using MATLAB/SIMULINK software.
Study on the Fused Deposition Modelling In Additive ManufacturingIJERD Editor
Additive manufacturing process, also popularly known as 3-D printing, is a process where a product
is created in a succession of layers. It is based on a novel materials incremental manufacturing philosophy.
Unlike conventional manufacturing processes where material is removed from a given work price to derive the
final shape of a product, 3-D printing develops the product from scratch thus obviating the necessity to cut away
materials. This prevents wastage of raw materials. Commonly used raw materials for the process are ABS
plastic, PLA and nylon. Recently the use of gold, bronze and wood has also been implemented. The complexity
factor of this process is 0% as in any object of any shape and size can be manufactured.
Spyware triggering system by particular string valueIJERD Editor
This computer programme can be used for good and bad purpose in hacking or in any general
purpose. We can say it is next step for hacking techniques such as keylogger and spyware. Once in this system if
user or hacker store particular string as a input after that software continually compare typing activity of user
with that stored string and if it is match then launch spyware programme.
A Blind Steganalysis on JPEG Gray Level Image Based on Statistical Features a...IJERD Editor
This paper presents a blind steganalysis technique to effectively attack the JPEG steganographic
schemes i.e. Jsteg, F5, Outguess and DWT Based. The proposed method exploits the correlations between
block-DCTcoefficients from intra-block and inter-block relation and the statistical moments of characteristic
functions of the test image is selected as features. The features are extracted from the BDCT JPEG 2-array.
Support Vector Machine with cross-validation is implemented for the classification.The proposed scheme gives
improved outcome in attacking.
Secure Image Transmission for Cloud Storage System Using Hybrid SchemeIJERD Editor
- Data over the cloud is transferred or transmitted between servers and users. Privacy of that
data is very important as it belongs to personal information. If data get hacked by the hacker, can be
used to defame a person’s social data. Sometimes delay are held during data transmission. i.e. Mobile
communication, bandwidth is low. Hence compression algorithms are proposed for fast and efficient
transmission, encryption is used for security purposes and blurring is used by providing additional
layers of security. These algorithms are hybridized for having a robust and efficient security and
transmission over cloud storage system.
Application of Buckley-Leverett Equation in Modeling the Radius of Invasion i...IJERD Editor
A thorough review of existing literature indicates that the Buckley-Leverett equation only analyzes
waterflood practices directly without any adjustments on real reservoir scenarios. By doing so, quite a number
of errors are introduced into these analyses. Also, for most waterflood scenarios, a radial investigation is more
appropriate than a simplified linear system. This study investigates the adoption of the Buckley-Leverett
equation to estimate the radius invasion of the displacing fluid during waterflooding. The model is also adopted
for a Microbial flood and a comparative analysis is conducted for both waterflooding and microbial flooding.
Results shown from the analysis doesn’t only records a success in determining the radial distance of the leading
edge of water during the flooding process, but also gives a clearer understanding of the applicability of
microbes to enhance oil production through in-situ production of bio-products like bio surfactans, biogenic
gases, bio acids etc.
Gesture Gaming on the World Wide Web Using an Ordinary Web CameraIJERD Editor
- Gesture gaming is a method by which users having a laptop/pc/x-box play games using natural or
bodily gestures. This paper presents a way of playing free flash games on the internet using an ordinary webcam
with the help of open source technologies. Emphasis in human activity recognition is given on the pose
estimation and the consistency in the pose of the player. These are estimated with the help of an ordinary web
camera having different resolutions from VGA to 20mps. Our work involved giving a 10 second documentary to
the user on how to play a particular game using gestures and what are the various kinds of gestures that can be
performed in front of the system. The initial inputs of the RGB values for the gesture component is obtained by
instructing the user to place his component in a red box in about 10 seconds after the short documentary before
the game is finished. Later the system opens the concerned game on the internet on popular flash game sites like
miniclip, games arcade, GameStop etc and loads the game clicking at various places and brings the state to a
place where the user is to perform only gestures to start playing the game. At any point of time the user can call
off the game by hitting the esc key and the program will release all of the controls and return to the desktop. It
was noted that the results obtained using an ordinary webcam matched that of the Kinect and the users could
relive the gaming experience of the free flash games on the net. Therefore effective in game advertising could
also be achieved thus resulting in a disruptive growth to the advertising firms.
Hardware Analysis of Resonant Frequency Converter Using Isolated Circuits And...IJERD Editor
-LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region[5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
pair switches off and the other on for a slightest period of time. Hence, the isolator ckt here is associated with
each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits.
Simulated Analysis of Resonant Frequency Converter Using Different Tank Circu...IJERD Editor
LLC resonant frequency converter is basically a combo of series as well as parallel resonant ckt. For
LCC resonant converter it is associated with a disadvantage that, though it has two resonant frequencies, the
lower resonant frequency is in ZCS region [5]. For this application, we are not able to design the converter
working at this resonant frequency. LLC resonant converter existed for a very long time but because of
unknown characteristic of this converter it was used as a series resonant converter with basically a passive
(resistive) load. . Here, it was designed to operate in switching frequency higher than resonant frequency of the
series resonant tank of Lr and Cr converter acts very similar to Series Resonant Converter. The benefit of LLC
resonant converter is narrow switching frequency range with light load[6] . Basically, the control ckt plays a
very imp. role and hence 555 Timer used here provides a perfect square wave as the control ckt provides no
slew rate which makes the square wave really strong and impenetrable. The dead band circuit provides the
exclusive dead band in micro seconds so as to avoid the simultaneous firing of two pairs of IGBT’s where one
pair switches off and the other on for a slightest period of time. Hence, the isolator ckt here is associated with
each and every ckt used because it acts as a driver and an isolation to each of the IGBT is provided with one
exclusive transformer supply[3]. The IGBT’s are fired using the appropriate signal using the previous boards
and hence at last a high frequency rectifier ckt with a filtering capacitor is used to get an exact dc
waveform .The basic goal of this particular analysis is to observe the wave forms and characteristics of
converters with differently positioned passive elements in the form of tank circuits. The supported simulation
is done through PSIM 6.0 software tool
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International Journal of Engineering Research and Development (IJERD)
1. International Journal of Engineering Research and Development
e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com
Volume 8, Issue 9 (September 2013), PP. 36-41
36
An Approach to Identify a Model for Efficient Prediction
of Exchange Rates Using Setty Volatile Index (SVI)
Sai Prasad Setty
Computer Science & Engineering GMR Institute of Technology India
ABSTRACT
In recent years forecasting of financial data such as stock market, exchange rate, interest rate and bankruptcy has
been observed to be a potential field of research due to its importance in financial and managerial decision
making. Survey of existing literature reveals that there is a need to develop efficient forecasting models
involving less computational load and fast forecasting capability. Our proposed work aims to fulfill this
objective by analyzing and comparing different ANN and Fuzzy models with some specified attributes. These
networks involve nonlinear inputs and simple ANN structure with few neurons. The models are functional link
artificial neural network (FLANN) as well as Dynamic Radial Basis Functional Networks (RBF) model and
hybrid NEURO-FUZZY model.
These models have been tested to predict currency exchange rate between US dollar Indian Rupees and
Japanese Yen and also stock market data like US-RUPEE and IBM etc. The performances of the proposed
models have been evaluated through simulation and compared with those obtained from other models.
Experimental results are compared on basis of various parameters including Normalized Root Mean Square
Error (NRMSE), Mean Absolute Percentage Error (AMAPE), Volatility and Error Convergence. An approach is
designed with the key parameter SVI (Setty Volatility Index) to classify the dataset and further decide the right
model for the prediction problem.
I. INTRODUCTION
1.1 Introduction to Financial Prediction
Financial Forecasting or specifically Stock Market prediction or Exchange rate prediction is one of the
hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of
attractive benefits that it has to offer. Forecasting the price movements in financial markets has been a major
challenge for common investors, businesses, brokers and speculators. The primary area of concern is to
determine the appropriate time to buy, hold or sell. In their quest to forecast, the investors assume that the future
trends in the stock market are based at least in part on present and past events and data. However financial time-
series is one of the most „noisiest‟ and „non-stationary‟ signals present and hence very difficult to forecast.
Many researchers in the past have applied various statistical and soft computing techniques such as neural
networks to predict the movements in these finance market indices.
2. A Survey Of Existing Ann Models For Finance market Prediction
A lot of research has gone into the development of models based on a range of intelligent soft
computing techniques over the last two decades. Early models employed the Multi-Layer Perceptron (MLP)
architecture using Back propagation algorithm, while a lot of recent work is based on evolutionary optimization
techniques such as Genetic Algorithms (GA). This section describes briefly some of work that has gone into the
field of application of ANN to stock price prediction. In Japan, technology major Fujitsu and investment
company, Nikko Securities joined hands to develop a stock market prediction system for TOPIX, the Tokyo
based stock index, using modular neural network architecture [1]. Various economic and technical parameters
were taken as input to the modular neural network consisting of multiple MLP used in parallel. A study was
done on the effect of change of network parameters of an ANN Back propagation model on the stock price
prediction problem [2]. The paper gives insights into the role of the learning rate, momentum, activation
function and the number of hidden Neurons to the prediction. In addition to ANN using Back propagation, the
Probabilistic Neural Network (PNN) has also been employed to stock prediction [3]. In their work, the model is
used to draw up a conservative thirty day stock price prediction of a specific stock: Apple Computers Inc. Due
to their bulky nature owing to the large training data, the PNN are not popular among forecasters. In the process
lots of newer architectures came to the fore (Ornes & Sklansky) [4] in their paper present a Visual Neural
Network (VNN), which combines the ability of multi expert networks to give low prediction error rates with
visual explanatory power of nonlinear dimensionality reduction [5] and applied to the TOPIX Tokyo 7 Stock
Exchange). The simulations show that MBNN, based on the concept of Universal Learning Networks (ULN),
2. An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Svi
37
have higher accuracy of prediction than conventional NNs. In their paper, (Chen, Dong & Zhao, 2005) [6]
investigate how the seemingly chaotic behavior of stock market could be well represented using Local Linear
Wavelet Neural Network (LLWNN) technique. Hybrid architectures are also being deployed in recent times
(Raymond Lee, 2004) [7] propose a Hybrid Radial Basis function Recurrent Network (HRBFN) stock
prediction system called the iJADE stock advisor. The stock advisor was applied to major Hong Kong stocks
and produced promising results in terms of efficiency, accuracy and mobility. Another Hybrid AI approach to
the implementation of trading strategies in the S&P 500 index futures market is proposed by (Tsiah, Hsu &
Lai,) [8]. The Hybrid AI approach integrates the rule-based systems techniques with Reasoning Neural
networks (RN) to highlight the advantages and overcome the limitations of both the techniques. Hiemstra
proposes a fuzzy logic forecast support system to predict the stock prices using parameters such as inflation,
GNP growth, interest rate trends and market valuations [9]. According to the paper, the potential benefits of a
fuzzy logic forecast support are better decision making due to the model-based approach, knowledge
management and knowledge accumulation Another effort towards the development of fuzzy models for stock
markets has been made by (AlaaSheta, 2006) [10] using Takagi-Sugeno (TS) fuzzy models. Sheta uses the
model for two non-linear processes, one pertaining to NASA and the other to prediction of next week S&P 500
index levels. The application of evolutionary optimization techniques such as Genetic Algorithm has given an
entirely new dimension to the field of stock market prediction (Badawy, Abdelazim&Darwish) [11] conducted
simulations using GA to find the optimal combination of technical parameters to predict Egyptian stocks
accurately (Tan, Quek& Ng, 2005) [12] introduce a novel technique known as Genetic complementary
Learning (GCL) to stock market prediction and give comparisons to demonstrate the superior performance of
the method. GCL algorithm is a confluence of GA and hippocampal complementary learning. Another paper
introducing Genetic algorithm approach to instance selection (GAIS) (Kyoungjae-Kim, 2006) [13] for ANN in
financial data mining has been reported. Kim introduces this technique to select effective training instances out a
large training data set to ensure efficient and fast training for stock market prediction networks. The GA also
evolves the weights that mitigate the well-known limitations of the gradient descent algorithm. The study
demonstrates enhances prediction performance at reduced training time. A hybrid model proposed by (Kuo,
Chen & Hwang, 2001) [14] integrates GA based fuzzy logic and ANN. The model involves both quantitative
factors (technical parameters) and qualitative factors such as political and psychological factors. Evaluation
results indicate that the neural network considering both the quantitative and qualitative factors excels the neural
network considering only the quantitative factors both in the clarity of buying-selling points and buying, selling
performance. Another hybrid model involving GA proposed by (Hassan, Nath & Kirley, 2006) [15] utilizes the
strengths of Hidden Markov Models (HMM), ANN and GA to forecast financial market behavior. Using ANN,
the daily stock prices are transformed to independent sets of values that become input to HMM. The job of the
GA is to optimize the initial parameters of HMM. The trained HMM is then used to identify and locate similar
patterns in the historical data. A similar study investigates the effectiveness of a hybrid approach based on Time
Delay Neural Networks (TDNN) and GA (Kim & Shin, 2006) [16]. The GA is used to optimize the number of
time delays in the neural network to obtain the optimum prediction performance. The functional link ANN is a
novel single Neuron based architecture first proposed by Pao[17].This study proposes a Functional Link or
FLANN architecture based model to predict the movements of prices in the DJIA and S&P500 stock indices. A
NEURO-FUZZY system composed of an Adaptive NEURO FUZZY Inference System (ANFIS) controller used
to control the stock market process model, also identified using an adaptive NEURO-FUZZY technique, is
derived and evaluated for a variety of stocks [18]. Radial basis function (RBF) networks have advantages of
easy design, good generalization, strong tolerance to input noise, and online learning ability. This paper presents
a review on different approaches of designing and training RBF networks [19].
II. APPROACH
3.1.1 SVI Index for a data set
The nature of the data set is to be studied in prior. To analyze the nature many approaches are present
in the real world. One fetching technique is volatility. Volatility index is calculated as the maximum of return
value array. This indicates the extent to which the data set is instable. The new volatility index (averaged value
of normal volatility) of a dataset is key parameter in our approach to classify the dataset in to one group based
on which the right model can be determined. The below is an illustration for plot of SVI index for US-RUPEE
dataset.
3. An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Svi
38
Fig 1(a), 1(b): plot of normalized us-rupee data set and volatility plot.
Calculation of normal volatility index
The formula for this is calculated as logarithmic difference of two successive values. This is
represented as an array. The maximum of that array (v1) is volatility index of the data range.
Return value (i) = ln (i) - ln (i-1). (1)
In case of US-RUPEE dataset which is plotted below is (0.18-(-0.3)) =0.48.
Setty volatility index (SIV)
In this new index average of first eight maximum volatile values are considered as the actual volatility index.
The v1, v2, v3, v4, v5, v6, v7, v8 when averaged gives the actual value called SVI.
SVI value very short
(next day)
short term(up to 2
months)
Long term( 3 months to 1
year)
low (-0.2
to +0.2)
NEURO-FUZZY
Works well
FLANN works very
well
FLANN is used widely with
many Technical indicators.
Online RBF May also be
used
high
(other
values)
NEURO-
FUZZY/FLANN
FLANN/Online RBF Online RBF is the only
choice in most cases
Table 1: proposed approach to decide the appropriate model
III. MODELS DESCRIPTION
4.1 Introduction to Online RBF
Radial basis function (RBF) networks have advantages of easy design, good generalization, strong
tolerance to input noise, and online learning ability. The properties of RBF networks make it very suitable to
design flexible control systems. This project presents a review on different approaches of designing and training
RBF networks. The recently developed algorithm is introduced for designing compact RBF networks and
performing efficient training process. At last, several problems are applied to test the main properties of RBF
networks like generalization ability, tolerance to input noise, and online learning ability. RBF networks are also
compared with traditional neural networks and fuzzy inference systems
Structure of Online RBF
Fig 2. Structure of standard RBF model with three layers
0 500 1000 1500 2000 2500 3000
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Data index number
DataIndexvalue
US RUPEE Data Set
original
0 100 200 300 400 500 600 700 800 900 1000
-1.5
-1
-0.5
0
0.5
1
1.5
2
volatality estimtion
4. An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Svi
39
Levenberg–Marquardt Algorithm
The update rule of Levenberg–Marquardt algorithm is
eJIJJ k
T
kkk kk
T
k
1
1
(2)
Where is combination coefficient, is the variable vector, e is error vector, and J is Jacobian matrix. This
algorithm is used in our Online-RBF model.
4.2Introduction to NEURO-FUZZY System
A NEURO-FUZZY system composed of an Adaptive NEURO FUZZY Inference System (ANFIS)
controller used to control the stock market process model, also identified using an adaptive NEURO-FUZZY
technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the
Efficient Market Hypothesis(EMH)by demonstrating much improved and better predictions, compared to other
approaches of short-term stock market trends and in particular the next day‟s trend of chosen stocks. The ANFIS
controller and the stock market process model inputs are chosen based on a comparative study of fifteen
different combinations of past stock prices performed to determine the stock market process model inputs
that return the best stock trend prediction for the next day in terms of the minimum Root Mean Square Error
(RMSE).Guassian-2 shaped membership functions are chosen over bell shaped Gaussian and triangular ones to
fuzzify the system inputs due to lowest RMSE. Real case studies
using data from emerging and well developed stock markets-the Athens and the NewYork Stock
Exchange(NYSE)-to train and evaluate the proposed system illustrate that compared to the ”buy and hold”
strategy and several other reported methods, the proposed approach and the forecasting trade accuracy are by far
superior.
The Structure of NEURO-FUZZY Model
The methodology presented in this paper considers historical/past stock prices as inputs (predictors) to
create a forecasting system that captures the underling “laws of the stock market price motion” thus, predicting
next day‟s trend of a stock. The proposed NEURO-FUZZY model uses an ANFIS technique which is superior
in modeling time series data as shown in Abraham et al. (2005), jang et al. (1997). A block diagram of the
proposed NEURO-FUZZY system, during the training and the application-evaluation phase is shown in Fig
3.2(a) and 3.2(b).
Learning in NEURO-FUZZY Model
The parameters set {p, q, r} in functional formula of every neuron is updated with the help of learning
law called “Woodrow’s Learning Law”. The formula is given by equation below
axbw *
(3)
Where ƞ is the learning rate parameter, b is
the actual output and x is the calculated output, b-x is the error generated by the network and a is the input
applied
IV. EXPERIMENTS AND RESULTS
5.1. Experiment for Long Term Prediction
Prediction problem is three types very short term prediction, short term prediction and long term
prediction. In case of long term prediction we have to test data over range of 90 days to 360 days. Here we
implemented three models which clearly indicate the prediction over US-RUPEE data set for 1000 points in
training phase and for 100 data we go for testing to check the performance of the network where the parameters
are freezed. The models are FLANN, Online RBF and NEURO-FUZZY model. The table below shows that
Online RBF performs well in case of long term predictions.
Model/parameter FLANN
NEURO-
FUZZY
Online RBF
NRMSE -1.1099e+04 -6.9065e+002 -3.7020e+009
MAPE -0.2024 -1.202 -15.0480
AMAPE 7.3824 8.2999 5.6402
Table 2: Table showing various performance measures of 3 models.
5. An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Svi
40
5.2. Experiment for Very Short Term Prediction
In case of very short term predictions we go for hit ratio. Here we implemented two models which
clearly indicate the prediction over US-RUPEE data set for 1000 points in training phase and for 20 data we go
for testing to check the performance of the network where the parameters are freezed. The models are FLANN,
NEURO-FUZZY model.
Hit Ratio
In case of very short prediction generally we consider number of exact matches done in the range
which varies from 2 days to 20 days. The hits are crossovers indicated after identification with the help of
square blocks. The formula for hit ratio is given by
𝒉𝒊𝒕𝒓𝒂𝒕𝒊𝒐 =
𝒏𝒖𝒎𝒃𝒆𝒓𝒐𝒇𝒆𝒙𝒂𝒄𝒕𝒎𝒂𝒕𝒄𝒉𝒆𝒔
𝒕𝒐𝒕𝒂𝒍𝒏𝒖𝒎𝒃𝒆𝒓𝒐𝒇𝒅𝒂𝒕𝒂𝒕𝒆𝒔𝒕𝒆𝒅
(4)
Plot of NEURO-FUZZY and Online RBF performance (20 test data)
In these plots we observe that number hits for twenty data points tested in part of test case after training
phase of 1000 data are 8 and 4 and the hit ratios are 8/20 =0.40 and 4/20=0.2 for Neuro-fuzzy and online RBF
model respectively. The Neuro-fuzzy model out performs in case of very short term predictions. This
experiment when further extended to Flann model yielded 5/20=0.25. Thus Neuro fuzzy model is best for very
short term predictions in case of low SVI values.
4(a) 4(b)
Fig 4(a), 4(b). Plot of very short term prediction problem in NEURO-FUZZY model and Online RBF model
5.3 Discussion
The experiment for long term prediction shows that various performance measures like normalized root
mean square error, mean absolute percentage error, average mean absolute percentage error over US-RUPEE
dataset in case of long term prediction problem that online RBF performs at par with FLANN but, NEURO-
FUZZY model doesn‟t work well over US-RUPEE data set. Online RBF works well in both cases of data set
with less volatility (low SVI) and high volatility but FLANN is used only in case of low SVI value.
In experiment for very short term prediction, the case is very short term prediction problem , hit ratio which is
taken as measure of performance and accuracy says that NEURO-FUZZY model works well in case of very
short term prediction problem. The results of experiment indicate that NEURO-FUZZY outstands Online-RBF
model for SVI data sets. Flann model also can be preferred in case of high SVI value sets after further
experiments. Thus the tabulated results are proved valid.
REFERENCES
[1]. Kimoto,T., Asakawa K., Yoda M., Takeoka M., “Stock market prediction system with modular neural
networks”, IJCNN International Joint Conference on Neural Networks, 1990., 1990 17-21 June 1990
Page(s):1 - 6 vol.1
[2]. Clarence N.W. Tan and Gerhard E. Wittig, “A Study of the Parameters of a Back propagation Stock
Price Prediction Model”, Proceedings 1993 The First New Zealand International Two-Stream
Conference on Artificial Neural Networks and Expert Systems p. 288-91,1993
[3]. Tan, H.; Prokhorov, D.V.; Wunsch, D.C., II; “Conservative thirty calendar day stock prediction using a
probabilistic neural network”, Computational Intelligence for Financial Engineering, 1995.,
Proceedings of the IEEE/IAFE 1995 9-11 April 1995 Page(s):113 – 11742
0 2 4 6 8 10 12 14 16 18 20
0.075
0.08
0.085
0.09
0.095
0.1
0.105
Data index number
DataIndexvalue
target&predicted small test
preicted
original
6. An Approach to Identify a Model for Efficient Prediction of Exchange Rates Using Svi
41
[4]. Ornes, C.; Sklansky, J.; “A neural network that explains as well as predicts financial market behavior”,
Computational Intelligence for Financial Engineering 1997, Proceedings of the IEEE/IAFE 1997 24-25
March 1997 Page: 43 – 49.
[5]. Yamashita, T.; Hirasawa, K.; Jinglu Hu; “Application of multi-branch neural networks to stock market
prediction”, IEEE International Joint Conference on Neural Networks, 2005. IJCNN '05. Volume 4,
Aug. 2005 Page(s):2544 – 2548 vol. 4.
[6]. Yuehui Chen; Xiaohui Dong; Yaou Zhao; “Stock Index Modeling using EDA based Local Linear
Wavelet Neural Network”, International Conference on Neural Networks and Brain, 2005. Vol. 3, 13-
15 Oct. 2005 Page(s):1646 – 1650
[7]. Lee, R.S.T.; “ iJADE stock advisor: an intelligent agent based stock prediction system using hybrid
RBF recurrent network” , IEEE Transactions on Systems, Man and Cybernetics, Part A, Volume 34,
Issue 3, May 2004 Page(s):421 – 428.
[8]. Ray Tsaih, Yenshan Hsu, Charles C. Lai; “Forecasting S&P 500 stock index futures with a hybrid AI
system”, Decision Support Systems 23 1998. Pages: 161–174.
[9]. Hiemstra, Y.; “A stock market forecasting support system based on fuzzy logic”, Proceedings of the
Twenty-Seventh Hawaii International Conference on System Sciences, 1994. Vol. III: Information
Systems: Decision Support and Knowledge-Based Systems, Volume 3, 4-7 Jan. 1994 Page(s):281 –
287.43
[10]. Sheta, A.; “Software Effort Estimation and Stock Market Prediction Using Takagi-Sugeno Fuzzy
Models”, IEEE International Conference on Fuzzy Systems, 2006 July 16-21, 2006 Page(s):171 – 178.
[11]. Badawy, F.A.; Abdelazim, H.Y.; Darwish, M.G.; “Genetic Algorithms for Predicting the Egyptian
Stock Market”, 3rd International Conference on Information and Communications Technology, 2005.
Dec. 2005. P109 – 122
[12]. Tan, T.Z.; Quek, C.; Ng, G.S.; “Brain-inspired genetic complementary learning for stock market
prediction”, IEEE Congress on Evolutionary Computation, 2005. Volume 3, 2-5 Sept. 2005
Page(s):2653 - 2660 Vol. 3.
[13]. Kyoung-jae Kim; “Artificial neural networks with evolutionary instance selection for financial
forecasting”, Expert Systems with Applications 30, 2006 pages 519-526.
[14]. R.J. Kuo; C.H. Chen, Y.C. Hwang; “An intelligent stock trading decision support system through
integration of genetic algorithm based fuzzy neural network and artificial neural network”, Fuzzy Sets
and Systems 118 (2001) pages 21-45.
[15]. Md. Rafiul Hassan, BaikunthNath, Michael Kirley; “A fusion model of HMM, ANN and GA for stock
market forecasting”, Expert Systems with Applications 2006.
[16]. Hyun-jung Kim, Kyung-shik Shin, “A hybrid approach based on neural networks and genetic
algorithms for detecting temporal patterns in stock markets” , Applied Soft Computing 2006, March
2006. 44
[17]. Y-H. Pao, “Adaptive Pattern Recognition & Neural Networks”, Reading, MA; Addison-Wesley, 1989.
[18]. Hao Yu, TiantianXie, StanisławPaszczyñskiandBogdan M. Wilamowski, “Advantages of Radial Basis
Function Networks for Dynamic System Design” ieee transactions on industrial electronics, vol. 58, no.
12, december 2011
[19]. TiantianXie, Hao Yu, Joel Hewlett, Paweł Ró˙zycki, and Bogdan Wilamowski,”Fast and Efficient
Second-Order Method for Training Radial Basis Function Networks” ieee transactions on neural
networks and learning systems, vol. 23, no. 4, april 2012.
.