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

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raoofi ali; MOHAMMADI TEIMOUR

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Journal: Iranian Journal of Economic Research Year:2018 | Volume:23 | Issue:76 Page(s): 107-136

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Title

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

Author(s)

raoofi ali | MOHAMMADI TEIMOUR | Issue Writer Certificate

Keywords

Wavelet DecompositionQ2

Adaptive Neural Fuzzy Inference SystemsQ1

Soft ComputingQ1

De-NoisingQ1

Stock ExchangeQ2

Abstract

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.

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APA: Copy

raoofi, ali, & MOHAMMADI, TEIMOUR. (2018). Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems. IRANIAN ECONOMIC RESEARCH, 23(76 ), 107-136. SID. https://sid.ir/paper/2543/en

Vancouver: Copy

raoofi ali, MOHAMMADI TEIMOUR. Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems. IRANIAN ECONOMIC RESEARCH[Internet]. 2018;23(76 ):107-136. Available from: https://sid.ir/paper/2543/en

IEEE: Copy

ali raoofi, and TEIMOUR MOHAMMADI, “Forecasting Tehran Stock Exchange Index Returns Using a Combination of Wavelet Decomposition and Adaptive Neural Fuzzy Inference Systems,” IRANIAN ECONOMIC RESEARCH, vol. 23, no. 76 , pp. 107–136, 2018, [Online]. Available: https://sid.ir/paper/2543/en

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