Welcome to the InfoSci Platform
Reference Hub7
A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

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

Warut Pannakkong, Van-Hai Pham, Van-Nam Huynh
Copyright: © 2017 |Volume: 8 |Issue: 4 |Pages: 24
ISSN: 1947-8208|EISSN: 1947-8216|EISBN13: 9781522514145|DOI: 10.4018/IJKSS.2017100103
Cite Article Cite Article

MLA

Pannakkong, Warut, et al. "A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting." IJKSS vol.8, no.4 2017: pp.30-53. http://doi.org/10.4018/IJKSS.2017100103

APA

Pannakkong, W., Pham, V., & Huynh, V. (2017). A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting. International Journal of Knowledge and Systems Science (IJKSS), 8(4), 30-53. http://doi.org/10.4018/IJKSS.2017100103

Chicago

Pannakkong, Warut, Van-Hai Pham, and Van-Nam Huynh. "A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting," International Journal of Knowledge and Systems Science (IJKSS) 8, no.4: 30-53. http://doi.org/10.4018/IJKSS.2017100103

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.