Abstract
Time series forecasting research area generally aims at improving prediction accuracy. Discrete wavelet transform (DWT) has been applied to time series for decomposing it into approximation and detail. Nevertheless, typically, the property of the approximation and the detail are presumed as either linear or nonlinear. Actually, the purpose of the DWT is not decomposing the original time series into linear and nonlinear time series. Hence, this paper develops a new hybrid model of autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and the DWT without prior assumption on linear and nonlinear property of the approximation and the detail. The different Khashei and Bijari’s hybrid models involving the ARIMA and the ANN are built for the approximation and the detail in order to extract their both linear and nonlinear components and fit the relationship between the components as the function instead of additive relationship. Finally, the forecasted approximation and detail are combined to obtain final forecasting. The prediction capability of the proposed model is examined with two well-known time series: the sunspot and the Canadian lynx time series. The results show that the proposed model has the best performance in all two data sets and all three measures (i.e. MSE, MAE and MAPE).
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Pannakkong, W., Huynh, VN. (2017). A New Hybrid Linear-Nonlinear Model Based on Decomposition of Discrete Wavelet Transform for Time Series Forecasting. In: Chen, J., Theeramunkong, T., Supnithi, T., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2017. Communications in Computer and Information Science, vol 780. Springer, Singapore. https://doi.org/10.1007/978-981-10-6989-5_16
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DOI: https://doi.org/10.1007/978-981-10-6989-5_16
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