IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
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 identify trends in real-time 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 the same certainty.
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.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
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.
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.
Stock Market Prediction using Machine LearningAravind Balaji
REPO : https://github.com/rvndbalaji/StockMarketPrediction
Stock Market Prediction using Machine
This is a presentation on Stock Market Prediction application built using R.
This is a part of final year engineering project
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 identify trends in real-time 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 the same certainty.
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.
In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic.
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 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.
This document provides details about a project aimed at predicting stock market values using Hidden Markov Models. It includes an introduction describing the problem of stock market prediction and the suitability of HMMs for tackling the time-dependent nature of the problem. The document outlines the approach taken, which involves using the daily fractional change in stock value and fractional deviation of intra-day high and low values to train separate HMMs for different stocks. It then discusses testing the models on various stocks and comparing performance to other existing methods. Tables and figures are provided to illustrate the experimental setup, results, and risk analysis.
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
This document discusses using LSTM artificial intelligence to forecast stock prices. It developed a user interface using Streamlit and used steps like importing and cleaning data, splitting it into training and test sets, creating and training a model, making predictions, and evaluating and improving predictions. Future work includes predicting stock prices based on multiple factors and implementing different algorithms because different data requires different techniques.
This document describes a project to predict stock prices using the Long Short Term Memory (LSTM) algorithm with Least Mean Squares (LMS). The system architecture includes data preprocessing, normalization, the LMS algorithm, LSTM algorithm and error calculation. Literature on previous stock price prediction methods using LSTM is reviewed, including the working of LSTM gates. The project achieves over 95% accuracy on stock datasets for Google, Nifty50, TCS, Infosys and Reliance. Future work plans to extend the application to cryptocurrency prediction and add sentiment analysis.
This document describes a final year project by four students at Himalaya College of Engineering in Nepal to analyze and predict stock market prices using artificial neural networks. The project aims to develop a neural network model to forecast stock prices on the Nepal Stock Exchange. Various technical, fundamental, and statistical analysis methods are currently used to predict stock prices but with limited success due to the complex nature of financial markets. The project outlines the design of the neural network, selection of input parameters, data collection, model training and testing. The goal is to apply neural networks to help forecast stock prices in Nepal's stock market.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
This document discusses using machine learning techniques to predict stock price movements. Specifically, it uses logistic regression on historical SP500 data to predict if the stock price will be higher or lower in the following week. Variables like open, close, volume and technical indicators are used as predictors. The model is trained on 70% of the data and tested on 30%. It achieves an accuracy of 65% on the test set, indicating the model performs well at predicting price movements. A backtest shows the strategy following the model's signals outperforms simply buying and holding the SP500. The summary concludes the approach is simple yet effective for this prediction problem.
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
Stock Market Price Prediction Using Technical AnalysisASHEESHVERMA6
This document discusses using technical data analysis and artificial neural networks to predict stock prices. It outlines using techniques like long short-term memory and support vector machines on historical stock data to analyze trends and make predictions. The workflow involves collecting stock market and financial news data, preprocessing it, then using machine learning algorithms and visualizing the results with charts. The goal is to determine whether combining news and price quotes can help predict stock closing prices.
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
The document discusses predicting Google stock prices using machine learning algorithms. It explores using news sentiment data and historical stock prices to build regression and time series forecasting models. Multiple linear regression, ARIMA, and Holt-Winters exponential smoothing models were tested on the data, with ARIMA producing the best results with an RMSE of 123.73 on test data. The aim is to improve predictive accuracy of Google stock movements.
This document provides an overview of a project report on sales prediction using machine learning models. It describes using linear regression and logistic regression to analyze time series sales data and predict future sales and sales of specific products. It outlines collecting sales data, preprocessing the data to address issues, training a model on the data, and using the model for forecasting. The document discusses using these techniques on a dataset from a company to predict sales for upcoming time periods.
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
The document discusses using an ARIMA model to predict stock market trends based on time series data from the Indian stock market. It introduces time series forecasting and the ARIMA model. The methodology section describes identifying and selecting an ARIMA (0,1,0) model for Nifty and Sensex data based on ACF and PACF plots. The results section finds this model predicted trends with around 5% error. The conclusion states time series forecasting can efficiently predict Indian stock markets.
Abstract: This PDSG workship introduces basic concepts on Bellman Equations. Concepts covered are States, Actions, Rewards, Value Function, Discount Factor, Bellman Equation, Bellman Optimality, Deterministic vs. Non-Deterministic, Policy vs. Plan, and Lifespan Penalty.
Level: Intermediate
Requirements: Should have some prior familiarity with graph theory and basic statistics. No prior programming knowledge is required.
The document describes the railway reservation system in India and its booking processes. It has two main booking methods: counter booking, which involves booking tickets at reservation counters at stations or other locations, and online booking, which allows people to book tickets from home via the internet with credit cards. It then discusses the functions of the railway reservation system and users/admins. It outlines the hardware, software, and operating system requirements to run the system. Finally, it describes using the spiral software process model to develop the system in an iterative way with a focus on risk analysis and requirements capture.
•Forecasted weekly sales of Walmart during holiday and non-holiday weeks across various regions to discover which factors impact sales and derive actionable insights to enhance the sales
•Analyzed predictors using Mosaic plots, scatterplots, incorporated data cleansing through outlier rectification and feature selection in SAS JMP. Generated ensemble models such as Partition Tree, Random forest, KNN Cluster to compare accuracy scores and error metrics against linear models in SAS for accurate predictions in sales. Random Forest was identified as best model with 89% accuracy
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
Spiking neural network: an introduction IDalin Zhang
1) The document discusses Spiking Neural Networks (SNNs), which are a type of neural network that more closely mimic biological neural behavior.
2) It describes the Leaky Integrate-and-Fire (LIF) neuron model, which is commonly used in SNNs. The LIF model integrates inputs over time and generates spikes when the voltage exceeds a threshold.
3) Different encoding approaches are discussed for representing input data as spike trains, including rate coding, temporal coding, population coding, and the Hough Spiker Algorithm. These approaches transform real-valued inputs into spike timings.
Prediction of stock market index using neural networks an empirical study of...Alexander Decker
This document discusses using neural networks to predict stock market indexes. Specifically, it describes building an Error Correction Network (ECN) model to predict returns of the BSE stock index in India and two major stocks, using both technical and fundamental data as inputs. Daily predictions are made using a standard ECN structure, while weekly predictions use an extended ECN structure. The results for stock predictions are less convincing than for the index, but the ECN outperforms a naive prediction strategy. The document provides mathematical descriptions of ECN models and how they can be used for time series forecasting.
Saturn is the sixth planet from the sun and is known for its prominent ring system composed of rock and ice. It orbits the sun every 29.5 years and rotates faster than all planets except Jupiter, completing a day in around 10 hours. Saturn has over 60 moons, more than any planet besides Jupiter.
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.
This document provides details about a project aimed at predicting stock market values using Hidden Markov Models. It includes an introduction describing the problem of stock market prediction and the suitability of HMMs for tackling the time-dependent nature of the problem. The document outlines the approach taken, which involves using the daily fractional change in stock value and fractional deviation of intra-day high and low values to train separate HMMs for different stocks. It then discusses testing the models on various stocks and comparing performance to other existing methods. Tables and figures are provided to illustrate the experimental setup, results, and risk analysis.
Stock Price Trend Forecasting using Supervised LearningSharvil Katariya
The aim of the project is to examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical user-generated content to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data.
This document discusses using LSTM artificial intelligence to forecast stock prices. It developed a user interface using Streamlit and used steps like importing and cleaning data, splitting it into training and test sets, creating and training a model, making predictions, and evaluating and improving predictions. Future work includes predicting stock prices based on multiple factors and implementing different algorithms because different data requires different techniques.
This document describes a project to predict stock prices using the Long Short Term Memory (LSTM) algorithm with Least Mean Squares (LMS). The system architecture includes data preprocessing, normalization, the LMS algorithm, LSTM algorithm and error calculation. Literature on previous stock price prediction methods using LSTM is reviewed, including the working of LSTM gates. The project achieves over 95% accuracy on stock datasets for Google, Nifty50, TCS, Infosys and Reliance. Future work plans to extend the application to cryptocurrency prediction and add sentiment analysis.
This document describes a final year project by four students at Himalaya College of Engineering in Nepal to analyze and predict stock market prices using artificial neural networks. The project aims to develop a neural network model to forecast stock prices on the Nepal Stock Exchange. Various technical, fundamental, and statistical analysis methods are currently used to predict stock prices but with limited success due to the complex nature of financial markets. The project outlines the design of the neural network, selection of input parameters, data collection, model training and testing. The goal is to apply neural networks to help forecast stock prices in Nepal's stock market.
Stock market prediction using Twitter sentiment analysisjournal ijrtem
ABSTRACT : In a study, it was investigated relationship among stock market movement and Tweeter feed content. We are expecting to see if there is connection among sentiment information extracted from the Tweets using a Vader in predicting movements of stock prices. As a result it was obtained strong positive correlation with a coefficient of correlation to be 0.7815.
KEYWORDS : correlation, financial market, polarity, sentiment analysis, tweets
This document discusses using machine learning techniques to predict stock price movements. Specifically, it uses logistic regression on historical SP500 data to predict if the stock price will be higher or lower in the following week. Variables like open, close, volume and technical indicators are used as predictors. The model is trained on 70% of the data and tested on 30%. It achieves an accuracy of 65% on the test set, indicating the model performs well at predicting price movements. A backtest shows the strategy following the model's signals outperforms simply buying and holding the SP500. The summary concludes the approach is simple yet effective for this prediction problem.
STOCK MARKET PREDICTION USING MACHINE LEARNING METHODSIAEME Publication
Stock price forecasting is a popular and important topic in financial and academic
studies. Share market is an volatile place for predicting since there are no significant
rules to estimate or predict the price of a share in the share market. Many methods
like technical analysis, fundamental analysis, time series analysis and statistical
analysis etc. are used to predict the price in tie share market but none of these
methods are proved as a consistently acceptable prediction tool. In this paper, we
implemented a Random Forest approach to predict stock market prices. Random
Forests are very effectively implemented in forecasting stock prices, returns, and stock
modeling. We outline the design of the Random Forest with its salient features and
customizable parameters. We focus on a certain group of parameters with a relatively
significant impact on the share price of a company. With the help of sentiment
analysis, we found the polarity score of the new article and that helped in forecasting
accurate result. Although share market can never be predicted with hundred per-cent
accuracy due to its vague domain, this paper aims at proving the efficiency of Random
forest for forecasting the stock prices
Stock Market Price Prediction Using Technical AnalysisASHEESHVERMA6
This document discusses using technical data analysis and artificial neural networks to predict stock prices. It outlines using techniques like long short-term memory and support vector machines on historical stock data to analyze trends and make predictions. The workflow involves collecting stock market and financial news data, preprocessing it, then using machine learning algorithms and visualizing the results with charts. The goal is to determine whether combining news and price quotes can help predict stock closing prices.
The importance of this type of research in the telecom market is to help companies make more profit.
It has become known that predicting churn is one of the most important sources of income to Telecom companies.
Hence, this research aimed to build a system that predicts the churn of customers i telecom company.
These prediction models need to achieve high AUC values. To test and train the model, the sample data is divided into 70% for training and 30% for testing.
The document discusses predicting Google stock prices using machine learning algorithms. It explores using news sentiment data and historical stock prices to build regression and time series forecasting models. Multiple linear regression, ARIMA, and Holt-Winters exponential smoothing models were tested on the data, with ARIMA producing the best results with an RMSE of 123.73 on test data. The aim is to improve predictive accuracy of Google stock movements.
This document provides an overview of a project report on sales prediction using machine learning models. It describes using linear regression and logistic regression to analyze time series sales data and predict future sales and sales of specific products. It outlines collecting sales data, preprocessing the data to address issues, training a model on the data, and using the model for forecasting. The document discusses using these techniques on a dataset from a company to predict sales for upcoming time periods.
An intelligent scalable stock market prediction systemHarshit Agarwal
Comparitive study of stock market prediction system using ANN and GONN. Sentiment analysis also done on yahoo news feed. Deployment done on hadoop cluster.
The document discusses using an ARIMA model to predict stock market trends based on time series data from the Indian stock market. It introduces time series forecasting and the ARIMA model. The methodology section describes identifying and selecting an ARIMA (0,1,0) model for Nifty and Sensex data based on ACF and PACF plots. The results section finds this model predicted trends with around 5% error. The conclusion states time series forecasting can efficiently predict Indian stock markets.
Abstract: This PDSG workship introduces basic concepts on Bellman Equations. Concepts covered are States, Actions, Rewards, Value Function, Discount Factor, Bellman Equation, Bellman Optimality, Deterministic vs. Non-Deterministic, Policy vs. Plan, and Lifespan Penalty.
Level: Intermediate
Requirements: Should have some prior familiarity with graph theory and basic statistics. No prior programming knowledge is required.
The document describes the railway reservation system in India and its booking processes. It has two main booking methods: counter booking, which involves booking tickets at reservation counters at stations or other locations, and online booking, which allows people to book tickets from home via the internet with credit cards. It then discusses the functions of the railway reservation system and users/admins. It outlines the hardware, software, and operating system requirements to run the system. Finally, it describes using the spiral software process model to develop the system in an iterative way with a focus on risk analysis and requirements capture.
•Forecasted weekly sales of Walmart during holiday and non-holiday weeks across various regions to discover which factors impact sales and derive actionable insights to enhance the sales
•Analyzed predictors using Mosaic plots, scatterplots, incorporated data cleansing through outlier rectification and feature selection in SAS JMP. Generated ensemble models such as Partition Tree, Random forest, KNN Cluster to compare accuracy scores and error metrics against linear models in SAS for accurate predictions in sales. Random Forest was identified as best model with 89% accuracy
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
---------------------------------------------------------------
Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
---------------------------------------------------------------
Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
Spiking neural network: an introduction IDalin Zhang
1) The document discusses Spiking Neural Networks (SNNs), which are a type of neural network that more closely mimic biological neural behavior.
2) It describes the Leaky Integrate-and-Fire (LIF) neuron model, which is commonly used in SNNs. The LIF model integrates inputs over time and generates spikes when the voltage exceeds a threshold.
3) Different encoding approaches are discussed for representing input data as spike trains, including rate coding, temporal coding, population coding, and the Hough Spiker Algorithm. These approaches transform real-valued inputs into spike timings.
Prediction of stock market index using neural networks an empirical study of...Alexander Decker
This document discusses using neural networks to predict stock market indexes. Specifically, it describes building an Error Correction Network (ECN) model to predict returns of the BSE stock index in India and two major stocks, using both technical and fundamental data as inputs. Daily predictions are made using a standard ECN structure, while weekly predictions use an extended ECN structure. The results for stock predictions are less convincing than for the index, but the ECN outperforms a naive prediction strategy. The document provides mathematical descriptions of ECN models and how they can be used for time series forecasting.
Saturn is the sixth planet from the sun and is known for its prominent ring system composed of rock and ice. It orbits the sun every 29.5 years and rotates faster than all planets except Jupiter, completing a day in around 10 hours. Saturn has over 60 moons, more than any planet besides Jupiter.
This paper examines using different textual representations of financial news articles - bag of words, noun phrases, and named entities - to predict stock prices 20 minutes after an article is released, using support vector machines. The study finds that named entities outperform bag of words, the typical representation used. It introduces the topic of using textual analysis to predict stock prices and reviews prior literature on stock prediction using both fundamental and technical analysis approaches as well as textual data.
- Saturn has the most extensive ring system in the solar system, consisting of ice and dust particles orbiting the planet. It has several moons within its rings.
- Titan is Saturn's largest moon, composed of ice and rock. It is the only known moon with a dense nitrogen-rich atmosphere and contains lakes of liquid methane and ethane.
- Cassini's radar images provided the first evidence of liquid on Titan's surface in the form of large lakes, suggesting weather patterns similar to Earth's.
ESANN2006 - A Cyclostationary Neural Network model for the prediction of the ...Augusto Pucci, PhD
Air pollution control is a major environmental concern. The quality of air is an important factor for everyday life in cities, since it affects the health of the community and directly influences the sustainability of our lifestyles and production methods. In this paper we propose a cyclostationary neural network (CNN) model for the prediction of the NO2 concentration. The cyclostationary nature of the problem guides the construction of the CNN architecture, which is composed by a number of MLP blocks equal to the cyclostationary period in the analyzed phenomenon, and is independent of exogenous inputs. Some preliminary experimentation shows that the CNN model significantly outperforms standard statistical tools usually employed for this task.
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.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
The document discusses fuzzy rule-based systems and approximate reasoning. It describes how linguistic variables and fuzzy rules are used in fuzzy inference systems to model uncertainty. The key aspects covered include fuzzy rule formation and reasoning, the different types of Mamdani and Sugeno fuzzy inference systems, and the use of fuzzy logic in expert systems.
This chapter discusses several supervised learning networks, including perceptrons, Adaline, Madaline, backpropagation networks, and radial basis function networks. Perceptrons are the simplest form of neural networks and use a linear threshold unit to classify inputs. Backpropagation networks can solve non-linearly separable problems using gradient descent training over multiple layers. Radial basis function networks employ Gaussian kernel functions for classification and functional approximation tasks.
This document summarizes a project that uses machine learning to predict stock market performance based on news headlines. It discusses using natural language processing and logistic regression to analyze headlines and learn the impact of news on stock values. The methodology involves collecting training and test data, preprocessing the text, applying machine learning algorithms, and evaluating predictions against actual values. Advanced modeling with n-gram analysis improved the accuracy of predictions from 42% to a higher percentage.
DisEMBL - Artificial neural network prediction of protein disorderLars Juhl Jensen
12th International Conference on Intelligent Systems for Molecular Biology, Software demo, Scottish Exhibition & Conference Center, Glasgow, Scotland, July 29-August 4, 2004
Neural networks for the prediction and forecasting of water resources variablesJonathan D'Cruz
This document reviews the use of artificial neural networks (ANNs) for predicting and forecasting water resource variables. It outlines the key steps in developing ANN models, including choosing performance criteria, preprocessing and dividing data, determining appropriate model inputs and network architecture, optimizing connection weights through training, and validating models. Specifically, it focuses on feedforward networks with sigmoid transfer functions, which have been most widely used for predicting water resources variables.
Final Year Project Report for B.Tech on Neural NetworkAkash Bisariya
This document discusses applying neural networks to forecasting, specifically forecasting soccer match outcomes. It provides background on soccer/football including basic rules and roles. Neural networks and their studio Neuroph are introduced for solving classification problems. The role of prediction in football is growing as a business, transitioning from human analysis to more data-driven approaches using mathematical models and qualitative analysis to forecast match results.
Saturn is known for its prominent ring system. Early astronomers like Galileo first observed the rings, though they were not well understood until Christiaan Huygens correctly interpreted them as a ring system in 1659. The Cassini mission has returned stunning images of Saturn and its moons like Titan, greatly increasing scientific understanding of the Saturnian system.
This document outlines an approach to using machine learning algorithms like hidden Markov models to predict stock prices. It discusses how technical analysis and increasing computational power allows algorithms to analyze large datasets. Human analysis is still important for interpretation. The document then provides an overview of using a hidden Markov model to account for different strategies in the stock market and modeling price data more accurately over time.
This document describes research on using deep learning models to predict stock market movements based on news events. It presents a method to extract event representations from news articles, generalize the events, embed the events, and feed the embedded events into deep learning models. Experimental results show that using embedded events as inputs to convolutional neural networks achieved more accurate stock market predictions than baseline methods, and modeling long, mid, and short-term event effects further improved performance. The research demonstrates that deep learning can effectively capture hidden relationships between news events and stock prices.
The document provides information about the solar system from the website www.makemegenius.com. It includes definitions and descriptions of the sun, planets (Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune), asteroids, and the moon. Short summaries are provided for each celestial object along with the website address to find related science videos for kids. The goal is to help students gain a better understanding of the solar system through free educational videos.
This document discusses using hidden Markov models (HMM) for stock price prediction. HMMs can model time series data as a probabilistic finite state machine. The document explains that HMMs can handle new stock market data robustly and efficiently predict similar price patterns to past data. It provides an overview of HMM components like states, transition probabilities, and emission probabilities. The document also demonstrates building an HMM model on stock data using the RHMM package in R, including training the model with Baum-Welch and predicting state sequences with Viterbi.
Financial forecastings using neural networks pptPuneet Gupta
This document discusses using neural networks for financial forecasting. It explains that neural networks can be trained on nonlinear and non-stationary financial data to predict things like stock and commodity prices without restricting models. The document outlines different neural network architectures like NARX models and techniques like backpropagation for training. It also discusses challenges like limited data, noise and non-stationarity. The document demonstrates applying different preprocessing techniques like moving average, FFT and HHT to interest rate, stock and exchange rate data in neural networks, finding that preprocessing can improve performance.
IRJET - Stock Market Analysis and PredictionIRJET Journal
This document discusses using machine learning algorithms to analyze stock market data and predict future stock prices. It proposes collecting historical stock price and Twitter sentiment data and using recurrent neural networks and long short-term memory models to analyze the data and generate predictions and visualizations. The models would allow investors to make informed decisions about buying and selling stocks to potentially achieve returns on their investments.
Project report on Share Market applicationKRISHNA PANDEY
This is the proposal document for AVS Group of Technology service offering in the website design and development and custom web application development space. The document details our understanding of the brief, the objectives of the services suite, the methodology, and deliverable and commercials.
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.
IRJET - Company’s Stock Price Predictor using Machine LearningIRJET Journal
This document describes a system for predicting a company's stock price using machine learning algorithms. It discusses fetching stock data from websites using web crawling and the DOM tree algorithm. The data is then stored in a database and analyzed using backpropagation in a neural network. Stock prices are predicted using the k-nearest neighbors algorithm, which finds the k closest historical prices to make a prediction. The system aims to help investors better understand stock market movements and provide more accurate price predictions based on historical data.
IRJET- Stock Market Prediction using Financial News ArticlesIRJET Journal
This document presents a system for predicting stock prices using financial news articles and machine learning. It first extracts data from trusted news sources and cleans it using natural language processing. Sentiment analysis determines if the news is positive or negative. This polarity is input to a machine learning model, which uses an algorithm like linear regression to predict if a stock's price will increase in the next 10 minutes. The system was tested on a dataset divided into 70% for training and 30% for testing. Accuracy, specificity and precision metrics showed the system could successfully predict stock price movements based on analyzing financial news sentiment.
STOCK MARKET PREDICTION USING MACHINE LEARNING IN PYTHONIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. Specifically, it evaluates using support vector machines, random forests, and regression models. It finds that support vector regression with an RBF kernel performed best compared to other models at accurately predicting stock prices based on historical data. The paper also reviews several related works applying machine learning methods like neural networks and support vector machines to financial time series data for stock prediction.
IRJET- Stock Market Prediction using Deep Learning and Sentiment AnalysisIRJET Journal
This document proposes a model to predict stock prices using sentiment analysis of tweets and historical stock price data with deep learning. The model has two phases: phase 1 uses sentiment analysis to classify tweets as positive or negative and analyzes historical stock price trends, and phase 2 trains a deep learning model on the results from phase 1 to predict future stock prices. The model showed improved prediction accuracy and more consistent results compared to using either technique alone. It uses sentiment analysis to understand social media sentiment, historical analysis to understand stock price trends over time, and deep learning to better correlate sentiment and historical data to predict future stock prices.
This document discusses machine learning algorithms and their applications. It begins with an abstract discussing supervised, unsupervised, and reinforcement learning techniques. It then discusses machine learning in more detail, explaining that machine learning algorithms represent data instances with a set of features and classify instances based on their labels. The main focus is on supervised and unsupervised learning techniques and their performance parameters. It provides an overview of support vector machines, neural networks, and other machine learning algorithms. In summary, the document provides a survey of different machine learning techniques, how they work, and their applications.
The document proposes developing an artificial intelligence-based stock trading system using particle swarm optimization. It finds that using 30 neural networks with a 100-day moving time interval to select the top 3 stock picks daily based on the highest recommendations produces the most stable and profitable results. The system uses swarm intelligence to search for the globally best-performing neural network each day to make trading decisions.
Stock Market Prediction using Alpha Vantage API and Machine Learning AlgorithmIRJET Journal
This document discusses using machine learning algorithms and the Alpha Vantage API to predict stock market prices based on historical stock data. Specifically, it proposes using a Naive Bayes machine learning algorithm to predict future stock prices for a company based on historical data obtained from the Alpha Vantage API, including opening price, closing price, highest price, lowest price, volume, and adjusted closing price. The goal is to develop a system that provides accurate stock market predictions to help investors and traders maximize their profits by informing decisions to buy or sell stocks.
A survey on Machine Learning and Artificial Neural NetworksIRJET Journal
This research paper provides an overview of machine learning and artificial neural networks. It discusses various machine learning techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning. It also describes artificial neural networks and how they are used to mimic biological neural networks. The paper reviews several related works applying machine learning and neural networks to tasks like hydrological modeling, facial expression recognition, and cattle detection. It highlights advantages like improved accuracy and automation, as well as limitations like data and computational requirements. Overall, the paper aims to improve knowledge of machine learning and neural networks techniques and their applications.
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.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
This document discusses using machine learning techniques to predict stock market prices. It begins with an introduction to existing stock prediction methods like fundamental and technical analysis. The proposed system would use machine learning models to analyze historical stock price data and sentiment analysis of news articles to predict future stock prices, volatility, and market trends. The methodology section outlines different models, including using only historical prices, classifying sentiment of news, and aspect-based sentiment analysis. Features like stock price volatility, momentum, and index momentum would be used. The conclusion states that accurately predicting the complex stock market requires considering various factors.
Data-Driven Approach to Stock Market Prediction and Sentiment AnalysisIRJET Journal
This document discusses a data-driven approach to stock market prediction and sentiment analysis. It proposes combining recurrent neural networks with long short-term memory (RNN-LSTM) to predict stock prices based on historical data, and using support vector machines (SVM) to analyze sentiment from news headlines and predict how it may affect stock trends. The paper reviews several related works applying machine learning techniques like RNN, LSTM, and SVM to stock prediction and sentiment analysis. It aims to improve prediction accuracy by combining both historical data analysis and sentiment analysis of news articles.
IRJET- Stock Market Prediction using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to predict stock market prices. It proposes building a machine learning model that uses historical stock data to predict future stock prices. The model would go through preprocessing, processing, and regression analysis of the dataset to make predictions. Predicting stock market movements accurately is challenging, but this model aims to generate results using machine learning and deep learning algorithms on the dataset to help investors make trading decisions.
IRJET- Big Data Driven Information Diffusion Analytics and Control on Social ...IRJET Journal
This document discusses controlling the spread of fake or misleading information on social media. It proposes a system to analyze information diffusion on social networks, identify diffused data, and control the spread of fake diffused data. The system would extract data from social media, perform sentiment analysis to determine the veracity of information, and discard fake or untrustworthy information from the database to prevent further propagation. A variety of machine learning techniques could be used for the sentiment analysis, including naive Bayes classification, linear regression, and gradient boosted trees. The goal is to curb the spread of misinformation while still allowing the diffusion of real or truthful information.
AlgoB – Cryptocurrency price prediction system using LSTMIRJET Journal
This document describes a cryptocurrency price prediction system called AlgoB that uses an LSTM neural network model. The system was developed by four students to predict cryptocurrency prices with high accuracy. It takes historical price data as input and can predict future prices. The system uses libraries like NumPy, Pandas, TensorFlow and Matplotlib. It achieves 80% prediction accuracy, outperforming regression and tree models. The LSTM model is trained on price data and evaluates predictions against real prices. This helps traders understand market movements and identify good times to buy and sell cryptocurrencies.
STOCKSENTIX: A MACHINE LEARNING APPROACH TO STOCKMARKETIRJET Journal
This paper presents an approach for analyzing stock market news articles using web scraping, natural language processing, machine learning, and data visualization techniques. Key aspects of the approach include:
1) Web scraping is used to collect stock market news articles from various online sources.
2) Data preprocessing with Pandas cleans and structures the data. Sentiment analysis then categorizes the sentiment of each article as positive, negative, or neutral.
3) Matplotlib and other tools are used to visualize sentiment trends in an easily interpretable way to help identify patterns and aid decision making.
IRJET - House Price Prediction using Machine Learning and RPAIRJET Journal
This document discusses using machine learning and robotic process automation (RPA) to predict house prices. Specifically, it proposes using the CatBoost algorithm and RPA to extract real-time data for house price prediction. RPA involves using software robots to automate data extraction, while CatBoost will be used to predict prices based on the extracted dataset. The system aims to reduce problems faced by customers by providing more accurate price predictions compared to relying solely on real estate agents. It will extract data using RPA, clean the data, then apply machine learning algorithms like CatBoost to predict house prices based on various attributes.
Hudhud cyclone caused extensive damage in Visakhapatnam, India in October 2014, especially to tree cover. This will likely impact the local environment in several ways: increased air pollution as trees absorb less; higher temperatures without tree canopy; increased erosion and landslides. It also created large amounts of waste from destroyed trees. Proper management of solid waste is needed to prevent disease spread. Suggested measures include restoring damaged plants, building fountains to reduce heat, mandating light-colored buildings, improving waste management, and educating public on health risks. Overall, changes are needed to water, land, and waste practices to rebuild the environment after the cyclone removed green cover.
Impact of flood disaster in a drought prone area – case study of alampur vill...eSAT Publishing House
1) In September-October 2009, unprecedented heavy rainfall and dam releases caused widespread flooding in Alampur village in Mahabub Nagar district, a historically drought-prone area.
2) The flood damaged or destroyed homes, buildings, infrastructure, crops, and documents. It displaced many residents and cut off the village.
3) The socioeconomic conditions and mud-based construction of homes in the village exacerbated the flood's impacts, making damage more severe and recovery more difficult.
The document summarizes the Hudhud cyclone that struck Visakhapatnam, India in October 2014. It describes the cyclone's formation, rapid intensification to winds of 175 km/h, and landfall near Visakhapatnam. The cyclone caused extensive damage estimated at over $1 billion and at least 109 deaths in India and Nepal. Infrastructure like buildings, bridges, and power lines were destroyed. Crops and fishing boats were also damaged. The document then discusses coping strategies and improvements needed to disaster management plans to better prepare for future cyclones.
Groundwater investigation using geophysical methods a case study of pydibhim...eSAT Publishing House
This document summarizes the results of a geophysical investigation using vertical electrical sounding (VES) methods at 13 locations around an industrial area in India. The VES data was interpreted to generate geo-electric sections and pseudo-sections showing subsurface resistivity variations. Three main layers were typically identified - a high resistivity topsoil, a weathered middle layer, and a basement rock. Pseudo-sections revealed relatively more weathered areas in the northwest and southwest. Resistivity sections helped identify zones of possible high groundwater potential based on low resistivity anomalies sandwiched between more resistive layers. The study concluded the electrical resistivity method was useful for understanding subsurface geology and identifying areas prospective for groundwater exploration.
Flood related disasters concerned to urban flooding in bangalore, indiaeSAT Publishing House
1. The document discusses urban flooding in Bangalore, India. It describes how factors like heavy rainfall, population growth, and improper land use have contributed to increased flooding in the city.
2. Flooding events in 2013 are analyzed in detail. A November rainfall caused runoff six times higher than the drainage capacity, inundating low-lying residential areas.
3. Impacts of urban flooding include disrupted daily life, damaged infrastructure, and decreased economic activity in affected areas. The document calls for improved flood management strategies to better mitigate urban flooding risks in Bangalore.
Enhancing post disaster recovery by optimal infrastructure capacity buildingeSAT Publishing House
This document discusses enhancing post-disaster recovery through optimal infrastructure capacity building. It presents a model to minimize the cost of meeting demand using auxiliary capacities when disaster damages infrastructure. The model uses genetic algorithms to select optimal capacity combinations. The document reviews how infrastructure provides vital services supporting recovery activities and discusses classifying infrastructure into six types. When disaster reduces infrastructure services, a gap forms between community demands and available support, hindering recovery. The proposed research aims to identify this gap and optimize capacity selection to fill it cost-effectively.
Effect of lintel and lintel band on the global performance of reinforced conc...eSAT Publishing House
This document analyzes the effect of lintels and lintel bands on the seismic performance of reinforced concrete masonry infilled frames through non-linear static pushover analysis. Four frame models are considered: a frame with a full masonry infill wall; a frame with a central opening but no lintel/band; a frame with a lintel above the opening; and a frame with a lintel band above the opening. The results show that the full infill wall model has 27% higher stiffness and 32% higher strength than the model with just an opening. Models with lintels or lintel bands have slightly higher strength and stiffness than the model with just an opening. The document concludes lintels and lintel
Wind damage to trees in the gitam university campus at visakhapatnam by cyclo...eSAT Publishing House
1) A cyclone with wind speeds of 175-200 kph caused massive damage to the green cover of Gitam University campus in Visakhapatnam, India. Thousands of trees were uprooted or damaged.
2) A study assessed different types of damage to trees from the cyclone, including defoliation, salt spray damage, damage to stems/branches, and uprooting. Certain tree species were more vulnerable than others.
3) The results of the study can help in selecting more wind-resistant tree species for future planting and reducing damage from future storms.
Wind damage to buildings, infrastrucuture and landscape elements along the be...eSAT Publishing House
1) A visual study was conducted to assess wind damage from Cyclone Hudhud along the 27km Visakha-Bheemli Beach road in Visakhapatnam, India.
2) Residential and commercial buildings suffered extensive roof damage, while glass facades on hotels and restaurants were shattered. Infrastructure like electricity poles and bus shelters were destroyed.
3) Landscape elements faced damage, including collapsed trees that damaged pavements, and debris in parks. The cyclone wiped out over half the city's green cover and caused beach erosion around protected areas.
1) The document reviews factors that influence the shear strength of reinforced concrete deep beams, including compressive strength of concrete, percentage of tension reinforcement, vertical and horizontal web reinforcement, aggregate interlock, shear span-to-depth ratio, loading distribution, side cover, and beam depth.
2) It finds that compressive strength of concrete, tension reinforcement percentage, and web reinforcement all increase shear strength, while shear strength decreases as shear span-to-depth ratio increases.
3) The distribution and amount of vertical and horizontal web reinforcement also affects shear strength, but closely spaced stirrups do not necessarily enhance capacity or performance.
Role of voluntary teams of professional engineers in dissater management – ex...eSAT Publishing House
1) A team of 17 professional engineers from various disciplines called the "Griha Seva" team volunteered after the 2001 Gujarat earthquake to provide technical assistance.
2) The team conducted site visits, assessments, testing and recommended retrofitting strategies for damaged structures in Bhuj and Ahmedabad. They were able to fully assess and retrofit 20 buildings in Ahmedabad.
3) Factors observed that exacerbated the earthquake's impacts included unplanned construction, non-engineered buildings, improper prior retrofitting, and defective materials and workmanship. The professional engineers' technical expertise was crucial for effective post-disaster management.
This document discusses risk analysis and environmental hazard management. It begins by defining risk, hazard, and toxicity. It then outlines the steps involved in hazard identification, including HAZID, HAZOP, and HAZAN. The document presents a case study of a hypothetical gas collecting station, identifying potential accidents and hazards. It discusses quantitative and qualitative approaches to risk analysis, including calculating a fire and explosion index. The document concludes by discussing hazard management strategies like preventative measures, control measures, fire protection, relief operations, and the importance of training personnel on safety.
Review study on performance of seismically tested repaired shear wallseSAT Publishing House
This document summarizes research on the performance of reinforced concrete shear walls that have been repaired after damage. It begins with an introduction to shear walls and their failure modes. The literature review then discusses the behavior of original shear walls as well as different repair techniques tested by other researchers, including conventional repair with new concrete, jacketing with steel plates or concrete, and use of fiber reinforced polymers. The document focuses on evaluating the strength retention of shear walls after being repaired with various methods.
Monitoring and assessment of air quality with reference to dust particles (pm...eSAT Publishing House
This document summarizes a study on monitoring and assessing air quality with respect to dust particles (PM10 and PM2.5) in the urban environment of Visakhapatnam, India. Sampling was conducted in residential, commercial, and industrial areas from October 2013 to August 2014. The average PM2.5 and PM10 concentrations were within limits in residential areas but moderate to high in commercial and industrial areas. Exceedance factor levels indicated moderate pollution for residential areas and moderate to high pollution for commercial and industrial areas. There is a need for management measures like improved public transport and green spaces to combat particulate air pollution in the study areas.
Low cost wireless sensor networks and smartphone applications for disaster ma...eSAT Publishing House
This document describes a low-cost wireless sensor network and smartphone application system for disaster management. The system uses an Arduino-based wireless sensor network comprising nodes with various sensors to monitor the environment. The sensor data is transmitted to a central gateway and then to the cloud for analysis. A smartphone app connected to the cloud can detect disasters from the sensor data and send real-time alerts to users to help with early evacuation. The system aims to provide low-cost localized disaster detection and warnings to improve safety.
Coastal zones – seismic vulnerability an analysis from east coast of indiaeSAT Publishing House
This document summarizes an analysis of seismic vulnerability along the east coast of India. It discusses the geotectonic setting of the region as a passive continental margin and reports some moderate seismic activity from offshore in recent decades. While seismic stability cannot be assumed given events like the 2004 tsunami, no major earthquakes have been recorded along this coast historically. The document calls for further study of active faults, neotectonics, and implementation of improved seismic building codes to mitigate vulnerability.
Can fracture mechanics predict damage due disaster of structureseSAT Publishing House
This document discusses how fracture mechanics can be used to better predict damage and failure of structures. It notes that current design codes are based on small-scale laboratory tests and do not account for size effects, which can lead to more brittle failures in larger structures. The document outlines how fracture mechanics considers factors like size effect, ductility, and minimum reinforcement that influence the strength and failure behavior of structures. It provides examples of how fracture mechanics has been applied to problems like evaluating shear strength in deep beams and investigating a failure of an oil platform structure. The document argues that fracture mechanics provides a more scientific basis for structural design compared to existing empirical code provisions.
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1) A 6.0 magnitude earthquake occurred off the coast of Paradip, Odisha in the Bay of Bengal on May 21, 2014 at a depth of around 40 km.
2) Analysis of magnetic and bathymetric data from the area revealed the presence of major lineaments in NW-SE and NE-SW directions that may be responsible for seismic activity through stress release.
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Effect of hudhud cyclone on the development of visakhapatnam as smart and gre...eSAT Publishing House
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Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
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1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_________________________________________________________________________________________
Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 746
STOCK PREDICTION SYSTEM USING ANN
Mahesh Korade1
, Sujata Gutte2
, Asha Thorat3
, Rohini Sonawane4
1
Lecturer in Dept. of computer SITRC Nashik, University of Pune, Maharashtra, India
2
B.E.Student in Dept. of computer SITRC Nashik, University of Pune, Maharashtra, India
3
B.E.Student in Dept. of computer SITRC Nashik, University of Pune, Maharashtra, India
4
B.E.Student in Dept. of computer SITRC Nashik, University of Pune, Maharashtra, India
Abstract
Prediction of stock market returns is an important issue in finance. Artificial neural networks have been used in stock market
prediction during the last decade. Predicting anything is very hard especially if the relationship between the inputs and outputs are
non-linear in nature and stock price prediction is one of them. In this paper we have proposed stock prediction system, in this stock
price prediction using multi-layer feed forward Artificial Neural Network (ANN). In this model we have used back propagation
algorithm of ANN. As the closing price of any stock market already covers other attributes of the company, we have used historical
stock prices (closing) for training the neural network. This paper was aimed at finding the best model for the prediction of Stock
Exchange market values.
Keywords: Stock market prediction, neural network, back propagation, Client-server architecture, Neuron.
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1. INTRODUCTION
Normal human tendency is that make its life sophisticated and
easier. That's why many people research on technology which
makes its life easier. This age of modern generation, in this
new technologies are developed to solve human problem. So
one of the tremendous task is share market. It is the biggest
challenge in front of human to predict its future value because
the share market is important factor which affect business as
well as human life. Till now there are researches are done in
share market field .Like now days many web sites are
available for share market to help u ser. But that web site is
not sufficient for new user to take a decision about share.
Without any guided person he can‟t take proper decision about
share whether it sale or purchase so it may happen that the
misguidance by senior person.
So we design such a system which has intelligence like human
being. And this type of technology is called as Artificial
intelligence (AI).The artificial intelligence technology is
inspired by human neural system. The neuron in neural system
which is mainly effect in decision making .In this case neurons
work is very effective .The science is inspired by this system
and develop Artificial Neural Network (ANN) [1].
In this way the artificial neural network (ANN) is used to take
proper decision in prediction in share market. Like share
market prediction, there is whether forecasting [8]. The
weather forecasting is like share prediction .In this also, there
is huge amount of data which is in non-linear form. Since also
we can predict whether at some level .In share market, by
using fix period of interval we can predict the future value of
particular share. For that the back propagation algorithm of
ANN is used.
2. RELATED WORK
The share market is worldwide famous. In it there is large
amount investment is happen. The shareholders are investing
money in shares. Our India‟s biggest share market is in
Mumbai. And share holders are invest their money in many of
these companies. But all share holders are not always stay
there to take update about share. So from years ago, there are
some web sites are launched in market to give update to share
holder at any time where internet is available. That web site
are only give information about share market like
www.sharekhan.com.So if any new person wants to enter in
the share market then that person get information from
knowledgeable person who is working in share market. And
new person cannot take proper decision about share without
guideline of knowledgeable person. And there is no guarantee
of that person, there may be chances of misguide line and new
person is goes in loss. So there is no software/website that
helps to new user. And no one is predicting share value to help
new share holder.
Stock market prediction has always had a certain issue for
researchers. While numbers of scientific attempts have been
made on these issues, no method has been discovered to
accurately predict stock price movement [1]. The difficulty of
prediction lies in the complexities of modeling share market
dynamics. Even with a not having enough of unchanging
prediction methods, so there have been some mild successes
[6].
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_________________________________________________________________________________________
Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 747
One area of limited success in stock market prediction comes
from linear data. Information from quarterly reports or
breaking news stories can dramatically affect the share price
of a security. Most existing books and predicted information
on financial text mining relies on identifying a predefined set
of keywords and special way of machine learning. These
methods typically give a task weights to keywords in
proportion to the movement of a share price. These types of
analyses have shown a decided, but weak ability to predict
future event the direction of share prices. In this system we
generate share value which is very close to changes in share
market using artificial neural network and network is train by
data set [10].
We believe that combining more precise textual
representations with historical stock pricing information will
yield improved predictability results.
Fig -1: Block diagram of proposed system
The work flow of above block diagram is given below.
1. The share holder as well as company can register itself in
database.
2. The Sample database is input to stock prediction system.
Here value of share is predicted with the help of history
analysis using back propagation algorithm.
3. And predicted value of share is send to user by SMS alert
system.
4. The share holder can set threshold value in system to
automatically sale or purchase the share. For sale and perches
share, it require current rate of that share, so current rate is
input to automation.
5. a) If user want to sale particular share at particular value
only then he set the threshold value of that share in system.
As soon as the current value of share is equal to the threshold
or greater then it then automatically share is sale.
b) And if user wants to purchase the share of particular
company at particular value then he also set the value of it in
system and as soon as the current value of share is less than or
equal to the threshold value then that share is automatic
purchase. After sale and purchase of share, SMS alerts are
automatically send to user.
Algorithm:
//Problem Definition: How to predict the value of a allowance
by considering fixed period of market trend related with
allowance of specially great firm.
//Input: Sample database of company, Current Value of share.
//Output: predicted values as well as SMS alter.
Step 1: Obtain training patterns set.
Step 2: Create neural network model A: No of Input neurons,
neurons of latent layer, Output Neurons.
Step 3: Set learning rate ƞ and momentum rate α.
Step 4: Initialize all connection Wji , Wkj and bias weight θj
θk to random values.
Step 5: Set minimum error, Emin.
Step 6: Start training by applying input pattern one at a the
Layers after that calculate total error.
Step 7: Through output, hidden layer as well as adapt weight
compute back propagate error.
Step 8: Through output, input layer as well as adapt weight
compute back propagate error
Step 9: Check it Error<Emin
If not repeat Steps 6 to 9. If yes stop training.[15]
3. METHODOLOGY
To develop Stock prediction system, it requires some
parameter such as follows:
3.1 Data Set
To predict any value in any field, it required large collection
of data. By that data only system can predict its future value as
well as it required to train and validate the neural network. So
in this system, we required stock market (share market) data
which can take from Yahoo! Finance or IT or Non-IT
companies.
3.2 Data Preprocessing
The data must be prepared in a format that the neural networks
understand it. Because which data we take from various
stream that is in non-linear format which is not directly
understand by neural network. The performance of neural
network is depends on quality of data, therefore data which is
fed into the network that must be in well known format of
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network. For that we remove unwanted data from non linear
data and then normalized it in range -1 to 1 using min-max
normalization technique. Then data is in normalized form
which can be fed into the network. After getting output, the
output is also in normalized form. So to get actual output, the
output is demoralized. To train network, the whole data is
categorized into 3 subset: Set of training data, set of validating
data, and set of testing data.
3.3 Creation of Neural Network and Training
The neural network is created by input layer, hidden layer and
output layer. And also neural network is depending on other
parameters such as follows:
Layers in network
How many neurons in input layer?
How many neurons in hidden layer?
Rate of momentum
Network learning rate
Initially network was created with some initial values. There
must be network is train by back propagation algorithm,
because it use to improve performance of neural network. To
train network it require data set. By that data set network get
trained and give correct output after processing input data.
4. NEURAL NETWORK
The neural network is made up of no. of neuron. The idea of
neuron is arrive from human body. The human body contains
number of neurons which contain certain information and that
information passes to one neuron to another neuron. The
information is some processing output or outside information.
In that way the ANN is work .In ANN, there are multiple
neurons, if we consider mth
and nth
neuron then mth
neuron
functioning output if pass input to nth
neuron. So the
connection weight of neuron m and n is denoted as Wmn i.e.
W is weight. [14]
Inputs Outputs
Input Hidden Output
Layer Layer Layer
Fig -2: Architecture of a feed forward MLP
In our system, the feed forward multi layer neural network is
used. The ANN is made up of layers of neurons. In this, the
first layer is input layer which is connected to the input data.
After input layer, there could be one or more than one hidden
layers called middle layer. And last layer is output layer which
gives results. In feedback network, all the connections are
toward the output layer. So the feed forward perceptron
network is as below.
m Wmn n
.
Fig -3: Perceptron neuron‟s connections.
4.1 Client-Server Architecture:
Our stock prediction system is the client server based system.
The client server based means the one system is server and
other are the client. The database of share market is located at
server and client access required data through server URL. For
example, as we access Google database through
www.google.com URL like this share market user can access
share database by using simply URL of system.
Server
……..
Client 1 Client 2 ….. Client n
Fig -4 Client-Server architecture
4.2 Back Propagation
There is found many colorful history of Back propagation.
Without any confusion, recently the most popular and
repeatedly used neural network architecture is back
propagation. The back propagation networks can learn
complicated multidimensional mapping. For computing the
necessary corrections the back propagation algorithm is used.
in the following four steps The back propagation algorithm
can completed : first one is Feed-forward computation second
is to the output layer compute Back propagation , third step is
to the hidden layer compute Back propagation, forth step is
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updates Weight. When the value of the error function has
become sufficiently small the algorithm is stopped.
Till now, hundreds of neural network type has been proposed.
Due to this neural surrounding a goal are full extent studied
and given different names and you will refer them as ANN.
Back propagation is networking itself as well as learning and
training algorithm also. This network may be feed forward or
Multi-layer perceptions (MLPs). Common method of training
artificial neural network is an abbreviation for “back
propagation”. Back propagation is supervised learning
method. Back propagation requires set of data of output for „n‟
no. of inputs and then making the training set. For simple
pattern recognition and mapping back propagation networks
are ideal. In training there is given algorithm examples of what
require doing and it changes the network‟s weight. That‟s why
when training is finished, for a particular input it will give you
the required output [6].
Suppose in network x is a single real input and f is network
function. Then derivative of F(x) is computed in two phases
Feed-forward and Back propagation. Feed-forward: into the
network the input x is fed. Using this function derivatives are
evaluated at every node. Evaluated derivatives are stored.
Back propagation: at the output unit the constant 1 is fed and
run network in backward direction. All Incoming information
is stored and multiplied the value which is stored in the left
part of unit. After that the result is propagate to the left part of
the unit. Then at the input unit the result is collected.
4.3 Testing and Training
Using this system user can predict future value of share. User
can set automation for buy or sale the share. For the prediction
and automation there is need of company database. Main
purpose of this system implementation is prediction. For this
first of all given database as a input to the prediction system
and input data are must be valid. Because of analysis database
system computes the result. if input data are not valid then
system cant compute correct result. That‟s why validity of
data is check first and after that valid data are given as an
input to the system. For result processing are done on input
database. Shareholder decides that which share they want to
purchase or sale. User can purchase share of any company
whose shares are available in market. And also shareholder
decides how many share wants to sale. The automation
function is used for automatically sale or purchase share. For
example: one shareholder wants to purchase share of Tata
company. Then shareholder can set value of no of share they
want to purchase and rate of share. And suppose some other
shareholder wants to sale the share of Tata company then he
set how many share he want to sale and rate of share. Rate of
share are continuously changed. Values of share are set by
admin so it is centralized process. Admin set value of share
and at that time if both value are match i.e. rate of share and
no of shares then automatically share is buy or sale share[12].
For knowing future value of share prediction function is
available. Prediction is computing by analysis history of
database. Back propagation is used for prediction. Shareholder
set the value of from how many days they want prediction.
Input is given to prediction system by share owner. In back
propagation there is used last value of share at everyday and
calculates difference between these value of share after that
calculate average and this average are add into current value
of share value. After addition getting answer is set as
prediction value. Thus predict future value of share [12].
5. CONCLUSIONS
In this paper we use artificial neural network (ANN) to predict
the value of stock share in the next day using the previous
history of data about stock market value. The resultant value
show that predicting the direction of changes of share values
in the next day. In this system we used feed forward MLP
neural network predicts the direction of the changes correctly;
the amount of change is very close to real share value. In
future works, we are going to apply other recently proposed
regression method which is newer in the field of machine
learning researches e.g. Support Vector Regression model and
claimed to have good generalization ability due to application
of large margin concept.
ACKNOWLEDGEMENTS
We are thankful to our Principal Dr. S.T. Gandhe, whose time
to time help and valuable guidance gave us high moral
support. We would also like to sincerely thank our Co-
coordinator of Computer Department Prof. Sandip Walunj for
his guidance and constant encouragement. We are also
grateful to Prof. Mahesh Korade and Prof. Monika Deshmukh
for their encouraging guidance. We are highly obliged to the
entire staff of the Computer department and our friend for
their kind cooperation and help.
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BIOGRAPHIES
Prof Mahesh Korde, Author is currently an assistant professor
at the Department of Computer Science and Engineering at the
University of Pune. His recent work has been in the areas of
Advance Database, Software testing and quality assurances.
Author is currently a B.E. final year student at
the Department of Computer Science and
Engineering at the University of Pune. His recent
work has been in the areas of Artificial Neural
Network.
Author is currently a B.E. final year student at
the Department of Computer Science and
Engineering at the University of Pune. His
recent work has been in the areas of Artificial
Neural Network.
Author is currently a B.E. final year student at the Department
of Computer Science and Engineering at the University of
Pune. His recent work has been in the areas of Artificial
Neural Network.