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A Comparative Simulation Study of ARIMA and Computational Intelligent Techniques for Forecasting Time Series Data |
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PP: 1-7 |
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doi:10.18576/jsapl/090101
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Author(s) |
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Haitham Fawzy,
EL Houssainy A. Rady,
Amal Mohamed Abdel Fattah,
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Abstract |
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| This paper aims to use the computational intelligent techniques and hybrid models for forecasting time series data based on 100 generated data of the Autoregressive integrated moving average (ARIMA) models. There are three scenarios used in this study. Furthermore, the performances were evaluated based on three metrics mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) to determine the more appropriate method and performance of model. The results of this study show that the hybrid ARIMA-ANN model was better than other models. The results also proved that applying hybrid models can improve the forecasting accuracy over the ARIMA and ANN models.
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