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...
| Main Authors: | , |
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| Format: | Article |
| Language: | fas |
| Published: |
Allameh Tabataba'i University Press
2018-11-01
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| Series: | Faṣlnāmah-i Pizhūhish/hā-yi Iqtiṣādī-i Īrān |
| Subjects: | |
| Online Access: | http://ijer.atu.ac.ir/article_9514_0d3693410ec996dff5f1ba438214ee58.pdf |
| Summary: | 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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| ISSN: | 1726-0728 |