Akintunde mutairu oyewale
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Journal 04JOURNAL 08
| ACKNOWLEDGMENT: The author wishes to express gratitude to the anonymous reviewers and the publisher for publishing this paper at no cost. I appreciate your assistance very much
5.0 REFERENCES [1]. Awajan, A. M., Ismail, M. T., & Al Wadi, S. (2017a). A hybrid EMD-MA for forecasting stock market index. Italian Journal of Pure and Applied Mathematics, 38(1), 1-20. [2]. Awajan, A. M., Ismail, M. T., & Wadi, S. A. (2017b). Forecasting time series using EMD- HWbagging. International Journal of Statistics & Economics, 18(3), 9-21. [3]. Bozarth CB and Handfield RB (2016). Introduction to operations and supply chain management, 4th ed. Raleigh: North Carolina State University. [4]. Javier, C., Rosario, E., Francisco, J. N., & Antonio, J. C. (2003). ARIMA models to predict next electricity price.IEEE Transactions on Power Systems, 18(3), 1014-1020. [5]. Khashei, M., Bijari, M., &Ardali, G. A. R. (2009). Improvement of auto-regressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Neuro- computing, 72(4-6), 956-967. [6]. Khashel, M., Bijari, M., &Ardali, G. A. R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers & Industrial Engineering, 63(1), 37-45. [7]. Kurawarwala AA and Matsuo H. (1998) Product growth models for medium-term forecasting of short life cycle products. Technol Forecast Soc Chang; 57: 169–196. [8]. Lee, C., & Ho, C. (2015). Short-term load forecasting using lifting scheme and ARIMA model. Expert System with Applications, 38(5), 5902-5911. [9]. Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models. Central Bank of Ireland Research Department, Technical Paper, 3/RT/1998. [10]. Miller D and Williams D. (2003) Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy. Int Journal of Forecast; 19: 669–684. [11]. Hyndman R.J. (2004). The interaction between trend and seasonality. International Journal of Forecast; 20(4): 561–563. [12]. Wang, J. J., Wang, J. Z., Zhang Z. G., & Guo, S. P. (2012). Stock index forecasting based on a hybrid model. Omega, 40, 758-766. [13] Vaccaro, A., El-Fouly, T. H. M., Cañizares, C. A., & Bhattacharya, K. (2015, June). Local learning-ARIMA adaptive hybrid architecture for hourly electricity price forecasting in Power Tech, 2015 IEEE indhoven (pp. 1-6). IEEE. [14]. Yunus, K., Thiringer, T., & Chen, P. (2016). ARIMA-based frequency-decomposed modeling of wind speed time series. IEEE Transactions on Power Systems,31(4), 2546-2556. Download 165.69 Kb. Share with your friends:
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