Clustering and prediction of time series data

Clustering and prediction of time series data

For years people have been looking at the stock market and wondered if it was possible to figure out the growth of the prices. The stock market, like most other time series phenomenon, runs on cycles. After every bull market, there is a bear market and after every bear, there will be a bull market....

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Main Author: Tan, Benjamin Bo Hong
Other Authors: Chan Chee Keong
Format: Final Year Project
Language:English
Published: 2014
Subjects:
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:http://hdl.handle.net/10356/61453
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-614532023-07-07T17:30:47Z Clustering and prediction of time series data Tan, Benjamin Bo Hong Chan Chee Keong School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence For years people have been looking at the stock market and wondered if it was possible to figure out the growth of the prices. The stock market, like most other time series phenomenon, runs on cycles. After every bull market, there is a bear market and after every bear, there will be a bull market. However despite knowing what will eventually happen, people are not able to predict when it will happen by using a standard mathematical formula and applying it to every single stock. In this paper I will make use of the method on segmentation and prediction of time series data to analyse the stock market. With these objectives in mind, I will be using the Perceptually Important Points approach to segment the stock market data and use the Time Delay Neural Network to train and thereafter predict the time series data. Past stock market data will be used to train the data and also to compare the accuracy of the programme. The results will be used to check for its validity and also suggest improvements for the time series analysis in subsequent projects Bachelor of Engineering 2014-06-10T06:49:22Z 2014-06-10T06:49:22Z 2014 2014 Final Year Project (FYP) http://hdl.handle.net/10356/61453 en Nanyang Technological University 51 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Tan, Benjamin Bo Hong
Clustering and prediction of time series data
description For years people have been looking at the stock market and wondered if it was possible to figure out the growth of the prices. The stock market, like most other time series phenomenon, runs on cycles. After every bull market, there is a bear market and after every bear, there will be a bull market. However despite knowing what will eventually happen, people are not able to predict when it will happen by using a standard mathematical formula and applying it to every single stock. In this paper I will make use of the method on segmentation and prediction of time series data to analyse the stock market. With these objectives in mind, I will be using the Perceptually Important Points approach to segment the stock market data and use the Time Delay Neural Network to train and thereafter predict the time series data. Past stock market data will be used to train the data and also to compare the accuracy of the programme. The results will be used to check for its validity and also suggest improvements for the time series analysis in subsequent projects
author2 Chan Chee Keong
author_facet Chan Chee Keong
Tan, Benjamin Bo Hong
format Final Year Project
author Tan, Benjamin Bo Hong
author_sort Tan, Benjamin Bo Hong
title Clustering and prediction of time series data
title_short Clustering and prediction of time series data
title_full Clustering and prediction of time series data
title_fullStr Clustering and prediction of time series data
title_full_unstemmed Clustering and prediction of time series data
title_sort clustering and prediction of time series data
publishDate 2014
url http://hdl.handle.net/10356/61453
_version_ 1772828264454160384

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