A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

Warut Pannakkong, Pham Van-Hai and Huynh Van-Nam
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Warut Pannakkong: School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan
Pham Van-Hai: Pacific Ocean University, Nha Trang, Vietnam
Huynh Van-Nam: School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Japan

International Journal of Knowledge and Systems Science (IJKSS), 2017, vol. 8, issue 4, 30-53

Abstract: This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

Date: 2017
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