Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
Agile analytics : An exploratory study of technical complexity managementAgnirudra Sikdar
The thesis involved the reviewing of various case studies to determine the types of modelling, choice of algorithm, types of analytical approaches and trying to determine the various complexities arising from these cases. From these reviews, procedures have been proposed to improve the efficiency and manage the various types of complexities from using agile methodological perspective. Focus was mostly done on Customer Segmentation and Clustering , with the sole purpose to bridge Big Data and Business Intelligence together using Analytic.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Gradient Based Power Line Insulator DetectionMD RAIHAN
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a method for insulator detection as well as pole detection. Sliding window based histogram oriented gradient (HOG) feature is extracted from the image, and the support vector machine is utilized for classifying each of those sliding windows. We trained our system with different features. We illustrate our approach on an evaluation set of 400 real-world insulator images captured from an automated vehicle. We got reasonable accuracy in both pole and insulator detection.
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Log Analytics in Datacenter with Apache Spark and Machine LearningPiotr Tylenda
Presented during DataMass Summit 2017.
http://summit2017.datamass.io/
https://www.youtube.com/watch?v=eGJfhHPdhuo
Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.
Gradient Based Power Line Insulator DetectionMD RAIHAN
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a method for insulator detection as well as pole detection. Sliding window based histogram oriented gradient (HOG) feature is extracted from the image, and the support vector machine is utilized for classifying each of those sliding windows. We trained our system with different features. We illustrate our approach on an evaluation set of 400 real-world insulator images captured from an automated vehicle. We got reasonable accuracy in both pole and insulator detection.
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
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
Towards a Unified Data Analytics Optimizer with Yanlei DiaoDatabricks
Today’s big data analytics systems are best effort only: despite the wide adoption, they still lack the ability to take user monetary constraints and performance goals, and automatically configure an analytic job to achieve those goals. Our work aims to take a step further towards building a new data analytics optimizer that works for arbitrary dataflow programs and determines the job configuration in an automated manner based on user objectives regarding latency, throughput, monetary cost, etc.
At the core of the optimizer are a principled multi-objective optimization framework that enables one to explore the tradeoffs between different objectives, and a deep learning-based modeling approach that can learn a model for each user objective as complex as necessary for the user computing environment. Using both SQL-like and machine learning jobs in Spark, we show that our techniques can learn a model of each objective with high accuracy, and the multi-objective optimizer can automatically recommend new configurations that significantly improve performance from the configurations manually set by engineers.
Towards explanations for Data-Centric AI using provenance recordsPaolo Missier
In this presentation, given to graduate students at Universita' RomaTre, Italy, we suggest that concepts well-known in Data Provenance can be exploited to provide explanations in the context of data-centric AI processes. Through use cases (incremental data cleaning, training set pruning), we build up increasingly complex provenance patterns, culminating in an open question:
how to describe "why" a specific data item has been manipulated as part of data processing, when such processing may consist of a complex data transformation algorithm.
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
1. The 9th International Conference on Computing and Information
Technology (IC2IT 2013)
KMUTNB
Dhaka Stock Exchange Trend Analysis Using Support
Vector Regression
Authors:
Phayung Meesad & Risul Islam Rasel
Faculty of Information Technology
King Mongkut’s University of Technology North Bangkok
Email: pym@kmutnb.ac.th; rasel.kmutnb@gmail.com
3. 1. Introduction
• Stock exchange :
is an emerging business sector has become more popular among the people.
many people, organizations are related to this business.
gaining insight about the market trend has become an important factor
• Stock trend or price prediction is regarding as challenging task because
Essentially a non-liner, non parametric
Noisy
Deterministically chaotic system
• Why deterministically chaotic system?
Liquid money and Stock adequacy
Human behavior and News related to the stock market
Share Gambling
Money exchange rate, etc.
9th May 20113
IC2IT 2013
3
4. 2. Related Work
2.1 Support Vector regression (SVR)
• Support vector machine (SVM), a novel artificial intelligence-based method
developed from statistical learning theory
• SVM has two major futures: classification (SVC) & regression (SVR).
• In SVM regression, the input is first mapped onto a m-dimensional feature space
using some fixed (nonlinear) mapping, and then a linear model is constructed in this
feature space.
• a margin of tolerance (epsilon) is set in approximation.
• This type of function is often called – epsilon intensive – loss function.
• Usage of slack variables to overcome noise in the data and non - separability
9th May 20113
IC2IT 2013
4
5. Related Work (cont..)
•
Support vector regression (SVR)
model with parameters w and b can be
expressed as
f(x) = w.z (x) + b
•
where y is the model output and input
x is mapped into a feature space by a
nonlinear function (x).
Image courtesy: Pao-Shan Yu*, Shien-Tsung Chen and I-Fan Chang
9th May 20113
IC2IT 2013
5
6. Related Work (cont..)
•
The regression problem of SVM can be expressed as the following optimization
problem.
l
min
1 | | w | | 2 + c / (p + p* )
* 2
w, b, p, p
i=1
subject to yi - (w.z (xi) + b) # f + p
(w.z (xi) + b) - yi # f + p*
p, p* $ 0, i = 1, 2, ......, l
•
•
*
Where pand p are slack variables that specify the upper and the lower training errors
subject to an error tolerance ε.
C is a positive constant that determines the degree of penalized loss when a training
error occurs
9th May 20113
IC2IT 2013
6
7. Related Work (cont..)
2.2 Windowing operator
•
•
•
•
The problem of forecasting the class attribute x, N steps ahead of time, as learning a
target function which uses a fixed number of M past values.
x(t+N) = f([x(t-0), x(t-1), …, x(t-M)]) ……………(1)
This equation can be written as:
x-0 = f([x-0, x-1, ..., x-M] – [x-0, x-1, …, x-N])
or as:
x-0= f([x-0, x-N, ..., x-M]) …………………(2)
or in the multivariate case as:
x-0 = f([x-0, y-0, x-1, y-1 ..., x-M, y-M] – [x-0, x-1, y-1, …, x-N, y-N]) …. (3)
Since Windowed Examples are of the form: [x-0, x-N, y-N, ..., x-M, y-M], we have
to remove all horizon attributes: [x-1, y-1, …, x-N, y-N].
The result is a dataset with Windowed Examples which can be fed to any machine
learning algorithm.
9th May 20113
IC2IT 2013
7
8. Notations:
•0 timestep 0,the timestep we wish to predict.
•N the number of timesteps between now and
0
•M the size of the window
•attribute-[0-9] a Windowed Attribute,
measured at timestep [0-9]
•x-0 attribute x measured at timestep 0
•x-0 equivalent to x-0
•x(t+N) equivalent to x(0), if t+N is the
timestep we wish to predict
9th May 20113
IC2IT 2013
8
9. Related Work (cont..)
2.3 Windowing operator:
transform the time series data into
a generic data set
convert the last row of a window
within the time series into a label
or target variable
Fed the cross sectional values as
inputs to the machine learning
technique such as liner regression,
Neural Network, Support vector
machine and so on.
9th May 20113
• Parameters:
Horizon (h)
Window size
Step size
Training window width
Testing window width
IC2IT 2013
9
11. Related Work (cont..)
2.4 Some recent research works
1. “Stock Forecasting Using Support Vector Machine,”
•
•
•
•
Authors: Lucas, K. C. Lai, James, N. K. Liu
Applied technique: SVM and NN
Data preprocess technique: Exponential Moving Average (EMA15) and relative
difference in percentage of price (RDP)
Domain: Hong Kong Stock Exchange
2. “Stock Index Prediction: A Comparison of MARS, BPN and SVR in an
Emerging Market,”
•
•
•
Authors: Lu, Chi-Jie, Chang, Chih-Hsiang, Chen, Chien-Yu, Chiu, Chih-Chou, Lee,
Tian-Shyug,
Applied technique: Multivariate adaptive regression splines (MARS), Back
propagation neural network (BPN), support vector regression (SVR), and multiple
linear regression (MLR).
Domain: Shanghai B-share stock index
9th May 20113
IC2IT 2013
11
12. Related Work (cont..)
3. “An Improved Support Vector Regression Modeling for Taiwan Stock
Exchange Market Weighted Index Forecasting,”
•
•
•
Authors: Kuan-Yu. Chen, Chia-Hui. Ho
Applied technique: SVR, GA, Auto regression (AR)
Domain: Taiwan Stock Exchange
• So, many research have been done using support vector machine (SVM) in
order predict stock market trend.
• GA, EMA, RDP and some other techniques have been used as input
selection technique or optimization technique. So, Still there are some
scope to apply different input selection or optimization technique to fed
input to the machine learning algorithm like support vector machine and
Neural network.
9th May 20113
IC2IT 2013
12
13. 3. Motivation & Goal
• Motivation:
SVR is a powerful machine learning technique for pattern recognition
Introducing of using different kinds of windowing function as data preprocess is a
new idea
Combining windowing function and support vector regression can make good
model for time series prediction.
• Goal:
Propose a good Win-SVR model to predict stock price
9th May 20113
IC2IT 2013
13
14. 4. Experiment Design
4.1 Data collection
Experiment dataset had been collected from Dhaka stock exchange (DSE),
Bangladesh.
4 year’s (January 2009-June 2012)historical data were collected.
Almost 522 company are listed in DSE. But for the convenient of the experiment
we only select one well known company data.
Dataset had 6 attributes. Date, open price, high price, low price, close price,
volumes.
5 attributes were used in experiment except volumes.
Total 822 days data. 700 data were used as training dataset, and 122 data were used
as testing dataset.
9th May 20113
IC2IT 2013
14
15. Experiment Design (cont..)
4.2 Model Work Flow
Training phase
• Step 1: Read the training dataset from local repository.
• Step 2: Apply windowing operator to transform the time
series data into a generic dataset. This step will convert
the last row of a window within the time series into a
label or target variable. Last variable is treated as label.
• Step 3: Accomplish a cross validation process of the
produced label from windowing operator in order to feed
them as inputs into SVR model.
• Step 4: Select kernel types and select special parameters
of SVR (C, ε , g etc).
• Step 5: Run the model and observe the performance
(accuracy).
• Step 6: If performance accuracy is good than go to step 6,
otherwise go to step 4.
• Step 7: Exit from the training phase & apply trained
model to the testing dataset.
Testing phase
• Step 1: Read the testing dataset from local repository.
• Step 2: Apply the training model to test the out of sample
dataset for price prediction.
• Step 3: Produce the predicted trends and stock price
9th May 20113
IC2IT 2013
15
16. Experiment Design (cont..)
4.3 Optimal Window settings
•
Three types of Windowing operator were used as data preprocess.
Normal rectangular window
Flatten window
De- flatten window
•
Optimal settings of windowing components for SVR models are given below:
Table 1: Window settings
Flatten window
De-Flatten window
9th May 20113
Step
size
Training
window
width
Test
window
width
All
3
1
30
30
3
1
30
30
5 days
8
1
30
30
22 days
Rectangular
Model
Window
size
1 day
Windowing
operator
25
1
30
30
All
5
1
30
30
IC2IT 2013
16
17. Experiment Design (cont..)
4.4 SVR kernel Parameters settings
•
•
•
•
Model 1: 1 day a-head prediction model
Model 2: 5 days a-head prediction model
Model 3: 22 days a-head prediction model
Kernel function: Radial basis function (RBF)
Table 2: SVR kernel parameters setting
SVR Model
Kernel
C
g
ε
ε+
ε-
Model-1
RBF
10000
1
2
1
1
Model-2
RBF
10000
1
2
1
1
Model-3
RBF
10000
1
2
1
1
9th May 20113
IC2IT 2013
17
18. Experiment Design (cont..)
4.5 Proposed Win-SVR Models
•
•
•
•
•
1 day & 5 days a-head model
Window type: Flatten Window
Window size : 3 (1 day model), 8 (5 days model)
Attribute selection : All
Step size : 1
T.W.W : 30, t.s.w : 30, Kernel type : RBF
Table 3: SVR model for Flatten window
Model
SV
Bias (b)
Weight (w)
w[open-2]
687
5 days
9th May 20113
3.335
-4.658
w[Close-2]
-746.516
-1074.989
-1087.763
-546.558
w[open-6]
w[High-7]
w[High-6]
1792.63
1716.616
2231.12
2447.79
w[Low-7]
696
w[Low-2]
w[open-7]
1 day
w[High-2]
w[Low-6]
w[Close-7]
w[Close-6]
2587.202
2219.727
2762.02
2187.662
IC2IT 2013
18
19. Experiment Design (cont..)
•
•
•
•
•
22 days a-head model
Window type: Normal Rectangular window
Window size : 3
Attribute selection : single attribute (close)
Step size : 1
T.W.W : 30, t.s.w : 30, Kernel type : RBF
Table 4: SVR model for normal rectangular window
Model
Support
Vector
Bias
(offset)
Weight (w)
SV
22 days
9th May 20113
b
w [close-2]
w [close-1]
w [close-0]
675
421.3
1719.6
1631.5
805.1
IC2IT 2013
19
20. 5. Result Analysis
Result evolution technique:
Error calculation: Used MAPE
MAPE: Mean Average Percentage Error (MAPE) was used to calculate the error rate
between actual and predicted price.
Here,
n
MAPE = 100
/| A - P
A
|
i=1
n
A = Actual price
P = Predicted price
n = number of data to be counted
9th May 20113
IC2IT 2013
20
21. Result Analysis (cont..)
Fig:2
Actual vs Predicted price
1 day a-head model
300
Close price (BDT)
Close Price (BDT)
Fig:1
250
200
150
100
50
0
300
250
200
150
100
50
0
1
8
15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120
Days (Jan-Jun'2012)
Actual
Fig:3
1
7
13
19 25 31 37
43 49 55 61
67 73 79 85
91 97 103 109
Days (Jan-jun'2012)
Predicted
Actual
Actual vs Predicted close price
22 days a-head model
Predicted
•Fig 1: 1 day a-head model result from flatten
window (MAPE : 0.04 )
300
Close Price (BDT)
Actual vs Predicted price
5 days a-head model
250
200
•Fig 2: 5 days a-head model result from flatten
window (MAPE : 0.15 )
150
100
50
0
1
6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96
Days (Jan-May'2012)
Actual
9th May 20113
•Fig 3: 22 days a-head model result from
rectangular window (MAPE : 0.22 )
Predicted
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22. Result Analysis (cont..)
Fig:4
Fig:5
Error rate
Normal Rectangular window
1
MAPE
MAPE
1.5
0.5
0
Jan
Feb
Mar
Apr
Error rate
Flatten window
0.50
0.40
0.30
0.20
0.10
0.00
Jan
May
Feb
Fig:6
5 days a-head
1 day a-head
22 days a-head
Error rate
De-flatten window
MAPE
Apr
May
Month
Month
1 day a-head
Mar
5 days a-head
22 days a-head
Table :Average MAPE (error) for test data (From Jan’12 to May’12)
Horizon
Rectangul
ar window
Flatten
window
Deflatten
window
1 Day a-head
16
14
12
10
8
6
4
2
0
1
0.42
0.04
7.79
5 days a-head
5
0.26
0.15
7.16
22 days
head
22
0.22
0.22
7.61
Model
Jan
Feb
Mar
Apr
May
Months
1 day a-head
9th May 20113
5 days a-head
22 days a-head
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23. 6. Conclusion
6.1 Discussions :
Different windowing function can produce different prediction results.
We used 3 types of windowing operators. Normal rectangular window, Flatten
window, De-flatten window.
Rectangular and flatten windows are able to produce good prediction result for time
series data.
De-flatten window can not produce good prediction results.
6.2 Limitations & Future works:
Here we only use 3 types of windowing operators.
Only one stock exchange data set were used to undertake the experiments.
Do not compare with other machine learning techniques.
In future, we will apply our model to other stock market data set and will also
compare our research result with other types of data mining techniques.
9th May 20113
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24. The 9th International Conference on Computing and Information
Technology (IC2IT 2013)
KMUTNB
THANK YOU
FOR YOUR ATTENTION
9th May 20113
IC2IT 2013
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