EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE

Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Emp...

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Autores principales:	Mohammad Raquibul Hossain, Mohd Tahir Ismail
Formato:	article
Lenguaje:	EN
Publicado:	UUM Press 2020
Materias:	forecasting stock price empirical mode decomposition intrinsic mode functions theta method time series Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/96581ed085524d54a4df21d3ed7dd851
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spelling	oai:doaj.org-article:96581ed085524d54a4df21d3ed7dd8512021-11-15T07:21:32ZEMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE10.32890/jict2020.19.4.41675-414X2180-3862https://doaj.org/article/96581ed085524d54a4df21d3ed7dd8512020-08-01T00:00:00Zhttp://e-journal.uum.edu.my/index.php/jict/article/view/jict2020.19.4.4https://doaj.org/toc/1675-414Xhttps://doaj.org/toc/2180-3862Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue. Then, the Theta method forecasted each decomposed subseries. Considering different forecast horizons, the effectiveness of this hybridisation was evaluated through values of conventional error measures found for test data and forecast data, which were obtained by adding forecast results for all component counterparts extracted from the EMD process. This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models. Mohammad Raquibul HossainMohd Tahir IsmailUUM Pressarticleforecasting stock priceempirical mode decompositionintrinsic mode functionstheta methodtime seriesInformation technologyT58.5-58.64ENJournal of ICT, Vol 19, Iss 4, Pp 533-558 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	forecasting stock price empirical mode decomposition intrinsic mode functions theta method time series Information technology T58.5-58.64
spellingShingle	forecasting stock price empirical mode decomposition intrinsic mode functions theta method time series Information technology T58.5-58.64 Mohammad Raquibul Hossain Mohd Tahir Ismail EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
description	Forecasting is a challenging task as time series data exhibit many features that cannot be captured by a single model. Therefore, many researchers have proposed various hybrid models in order to accommodate these features to improve forecasting results. This work proposed a hybrid method between Empirical Mode Decomposition (EMD) and Theta methods by considering better forecasting potentiality. Both EMD and Theta are efficient methods in their own ground of tasks for decomposition and forecasting, respectively. Combining them to obtain a better synergic outcome deserves consideration. EMD decomposed the training data from each of the five Financial Times Stock Exchange 100 Index (FTSE 100 Index) companies’ stock price time series data into Intrinsic Mode Functions (IMF) and residue. Then, the Theta method forecasted each decomposed subseries. Considering different forecast horizons, the effectiveness of this hybridisation was evaluated through values of conventional error measures found for test data and forecast data, which were obtained by adding forecast results for all component counterparts extracted from the EMD process. This study found that the proposed method produced better forecast accuracy than the other three classic methods and the hybrid EMD-ARIMA models.
format	article
author	Mohammad Raquibul Hossain Mohd Tahir Ismail
author_facet	Mohammad Raquibul Hossain Mohd Tahir Ismail
author_sort	Mohammad Raquibul Hossain
title	EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
title_short	EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
title_full	EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
title_fullStr	EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
title_full_unstemmed	EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE
title_sort	empirical mode decomposition based on theta method for forecasting daily stock price
publisher	UUM Press
publishDate	2020
url	https://doaj.org/article/96581ed085524d54a4df21d3ed7dd851
work_keys_str_mv	AT mohammadraquibulhossain empiricalmodedecompositionbasedonthetamethodforforecastingdailystockprice AT mohdtahirismail empiricalmodedecompositionbasedonthetamethodforforecastingdailystockprice
_version_	1718428510137090048

EMPIRICAL MODE DECOMPOSITION BASED ON THETA METHOD FOR FORECASTING DAILY STOCK PRICE

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