Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems

Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems

In this paper, a framework for time series prediction is presented which makes it possible to predict the future values of a time series more accurately using soft computing approach. In this method, input data of adaptive neural fuzzy inference systems are reduced using wavelet decomposition of ran...

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Main Authors: Ali Raoofi, Teimour Mohammadi
Format: Article
Language:fas
Published: Allameh Tabataba'i University Press 2018-11-01
Series:Faṣlnāmah-i Pizhūhish/hā-yi Iqtiṣādī-i Īrān
Subjects:
wavelet decomposition adaptive neural fuzzy inference systems soft computing de-noising stock exchange
Online Access:http://ijer.atu.ac.ir/article_9514_0d3693410ec996dff5f1ba438214ee58.pdf
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spelling doaj-066c04401a7046e887ac6ef0f165f3742020-11-25T03:06:49ZfasAllameh Tabataba'i University PressFaṣlnāmah-i Pizhūhish/hā-yi Iqtiṣādī-i Īrān1726-07282018-11-01237610713610.22054/IJER.2018.9514Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference SystemsAli Raoofi 0Teimour Mohammadi 1Ph.D. Candidate, Allameh Tabataba`i University, Tehran,Iran Associate Professor, Allameh Tabataba`i University, Tehran,IranIn this paper, a framework for time series prediction is presented which makes it possible to predict the future values of a time series more accurately using soft computing approach. In this method, input data of adaptive neural fuzzy inference systems are reduced using wavelet decomposition of random noises; therefore, it reduces errors and improves the desired chaotic time series prediction. The above method was evaluated using Tehran Stock Exchange return series for the period of 23/10/2009 to 23/3/2013, and the results indicate the superiority of the proposed method compared to other ones. http://ijer.atu.ac.ir/article_9514_0d3693410ec996dff5f1ba438214ee58.pdfwavelet decomposition adaptive neural fuzzy inference systems soft computing de-noising stock exchange
collection DOAJ
language fas
format Article
sources DOAJ
author Ali Raoofi
Teimour Mohammadi
spellingShingle Ali Raoofi
Teimour Mohammadi
Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
Faṣlnāmah-i Pizhūhish/hā-yi Iqtiṣādī-i Īrān
wavelet decomposition adaptive neural fuzzy inference systems soft computing de-noising stock exchange
author_facet Ali Raoofi
Teimour Mohammadi
author_sort Ali Raoofi
title Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
title_short Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
title_full Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
title_fullStr Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
title_full_unstemmed Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems
title_sort forecasting tehran stock exchange index returns using a combination of wavelet decomposition and adaptive neural fuzzy inference systems
publisher Allameh Tabataba'i University Press
series Faṣlnāmah-i Pizhūhish/hā-yi Iqtiṣādī-i Īrān
issn 1726-0728
publishDate 2018-11-01
description In this paper, a framework for time series prediction is presented which makes it possible to predict the future values of a time series more accurately using soft computing approach. In this method, input data of adaptive neural fuzzy inference systems are reduced using wavelet decomposition of random noises; therefore, it reduces errors and improves the desired chaotic time series prediction. The above method was evaluated using Tehran Stock Exchange return series for the period of 23/10/2009 to 23/3/2013, and the results indicate the superiority of the proposed method compared to other ones.
topic wavelet decomposition adaptive neural fuzzy inference systems soft computing de-noising stock exchange
url http://ijer.atu.ac.ir/article_9514_0d3693410ec996dff5f1ba438214ee58.pdf
work_keys_str_mv AT aliraoofi forecastingtehranstockexchangeindexreturnsusingacombinationofwaveletdecompositionandadaptiveneuralfuzzyinferencesystems
AT teimourmohammadi forecastingtehranstockexchangeindexreturnsusingacombinationofwaveletdecompositionandadaptiveneuralfuzzyinferencesystems
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