Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data

Time series analysis and prediction is a very important and active research area. In this age of profuse data generation, proper use of available data has become crucial in forecasting and decision making. This thesis presents the research study involving the development of five advanced forecasting...

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Main Author:	Hossain, Mohammad Raquibul
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA1 Mathematics (General)
Online Access:	http://eprints.usm.my/53227/1/MOHAMMAD%20RAQUIBUL%20HOSSAIN%20-%20TESIS24.pdf
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spelling	my-usm-ep.532272022-06-30T17:16:16Z Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data 2021-10 Hossain, Mohammad Raquibul QA1 Mathematics (General) Time series analysis and prediction is a very important and active research area. In this age of profuse data generation, proper use of available data has become crucial in forecasting and decision making. This thesis presents the research study involving the development of five advanced forecasting methods and their experimentation on twelve stock price time series datasets. Traditional forecasting methods have limitations in forecasting potentiality due to their linearity and stationarity assumptions on the datasets. However, real life data including stock price data have sophisticated features and patterns encompassing nonlinearity and non-stationarity. Therefore, there is the research scope to search for better methods to improve forecast accuracy obtainable from the traditional methods by applying advanced approaches. Empirical mode decomposition (EMD), a very essentially important part of Hilbert-Huang transforms (HHT) is a very adaptive decomposition algorithm to view data from granular and different time scales. Being a robust analysing tool in signal processing, EMD has been widely applied in other fields including economics and finance. However, there are still scopes in improving the forecast accuracy of nonlinear nonstationary financial time series using EMD and other forecasting methods. From such relevant hypotheses, this study was followed by three research objectives. Five EMD-based methods were developed on these objectives 2021-10 Thesis http://eprints.usm.my/53227/ http://eprints.usm.my/53227/1/MOHAMMAD%20RAQUIBUL%20HOSSAIN%20-%20TESIS24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia. Pusat Pengajian Sains Matematik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA1 Mathematics (General)
spellingShingle	QA1 Mathematics (General) Hossain, Mohammad Raquibul Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
description	Time series analysis and prediction is a very important and active research area. In this age of profuse data generation, proper use of available data has become crucial in forecasting and decision making. This thesis presents the research study involving the development of five advanced forecasting methods and their experimentation on twelve stock price time series datasets. Traditional forecasting methods have limitations in forecasting potentiality due to their linearity and stationarity assumptions on the datasets. However, real life data including stock price data have sophisticated features and patterns encompassing nonlinearity and non-stationarity. Therefore, there is the research scope to search for better methods to improve forecast accuracy obtainable from the traditional methods by applying advanced approaches. Empirical mode decomposition (EMD), a very essentially important part of Hilbert-Huang transforms (HHT) is a very adaptive decomposition algorithm to view data from granular and different time scales. Being a robust analysing tool in signal processing, EMD has been widely applied in other fields including economics and finance. However, there are still scopes in improving the forecast accuracy of nonlinear nonstationary financial time series using EMD and other forecasting methods. From such relevant hypotheses, this study was followed by three research objectives. Five EMD-based methods were developed on these objectives
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hossain, Mohammad Raquibul
author_facet	Hossain, Mohammad Raquibul
author_sort	Hossain, Mohammad Raquibul
title	Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
title_short	Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
title_full	Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
title_fullStr	Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
title_full_unstemmed	Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data
title_sort	improving time series models prediction based on empirical mode decomposition using stock market data
granting_institution	Universiti Sains Malaysia.
granting_department	Pusat Pengajian Sains Matematik
publishDate	2021
url	http://eprints.usm.my/53227/1/MOHAMMAD%20RAQUIBUL%20HOSSAIN%20-%20TESIS24.pdf
_version_	1747822231467065344

Improving Time Series Models Prediction Based On Empirical Mode Decomposition Using Stock Market Data

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