Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series
Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analy...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2020/5057801 |
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| author | Ghassane Benrhmach Khalil Namir Abdelwahed Namir Jamal Bouyaghroumni |
| author_facet | Ghassane Benrhmach Khalil Namir Abdelwahed Namir Jamal Bouyaghroumni |
| author_sort | Ghassane Benrhmach |
| collection | DOAJ |
| description | Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy. |
| format | Article |
| id | doaj-art-40b9fb693bda41d4a7fa9d444e9fcf35 |
| institution | DOAJ |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-40b9fb693bda41d4a7fa9d444e9fcf352025-08-20T02:39:13ZengWileyJournal of Applied Mathematics1110-757X1687-00422020-01-01202010.1155/2020/50578015057801Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time SeriesGhassane Benrhmach0Khalil Namir1Abdelwahed Namir2Jamal Bouyaghroumni3Laboratory of Analysis, Modelling and Simulation (LAMS), Faculty of Sciences Ben M’Sik, Hassan II University, P.O. Box 7955, Sidi Othman, Casablanca, MoroccoLaboratory of Information Technology and Modelling, Faculty of Sciences Ben M’Sik, Hassan II University, P.O. Box 7955, Sidi Othman, Casablanca, MoroccoLaboratory of Information Technology and Modelling, Faculty of Sciences Ben M’Sik, Hassan II University, P.O. Box 7955, Sidi Othman, Casablanca, MoroccoLaboratory of Analysis, Modelling and Simulation (LAMS), Faculty of Sciences Ben M’Sik, Hassan II University, P.O. Box 7955, Sidi Othman, Casablanca, MoroccoTime series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.http://dx.doi.org/10.1155/2020/5057801 |
| spellingShingle | Ghassane Benrhmach Khalil Namir Abdelwahed Namir Jamal Bouyaghroumni Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series Journal of Applied Mathematics |
| title | Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series |
| title_full | Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series |
| title_fullStr | Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series |
| title_full_unstemmed | Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series |
| title_short | Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series |
| title_sort | nonlinear autoregressive neural network and extended kalman filters for prediction of financial time series |
| url | http://dx.doi.org/10.1155/2020/5057801 |
| work_keys_str_mv | AT ghassanebenrhmach nonlinearautoregressiveneuralnetworkandextendedkalmanfiltersforpredictionoffinancialtimeseries AT khalilnamir nonlinearautoregressiveneuralnetworkandextendedkalmanfiltersforpredictionoffinancialtimeseries AT abdelwahednamir nonlinearautoregressiveneuralnetworkandextendedkalmanfiltersforpredictionoffinancialtimeseries AT jamalbouyaghroumni nonlinearautoregressiveneuralnetworkandextendedkalmanfiltersforpredictionoffinancialtimeseries |