Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data-Reference-Cited by-同舟云学术

Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data

Published:2016-09 Issue:3 Volume:2 Page:202-223
ISSN:2405-9188
Container-title:The Journal of Finance and Data Science
language:en
Short-container-title:The Journal of Finance and Data Science

Author:

Parida A.K.,Bisoi R.,Dash P.K.

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Science Applications,Economics and Econometrics,Finance,Business, Management and Accounting (miscellaneous),Statistics and Probability

Reference52 articles.

1. A novel hybridization of artificial neural networks and ARIMA models for time series forecasting;Khashei;Appl Soft Comput,2011

2. Forecast of hourly average wind speed with ARMA models in Navarre (Spain);Torres;Sol Energy,2005

3. A GARCH forecasting model to predict day-ahead electricity prices;Garcia;IEEE Trans Power Syst,2005

4. Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach;Wang;Expert Syst Appl,2009

5. Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries;Basel;Int J Forecast,2005

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