Combination of time series forecasts using neural network

• Published in: Proceedings of the 2011 International Conference on Electrical Engineering and Informatics pp. 1 - 6
• Main Authors: Widodo, A., Budi, I.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2011
• Subjects: Artificial neural networks; combination; ensemble; Forecasting; Hidden Markov models; Kernel; neural network; prediction; Predictive models; time series; Time series analysis; Training
• ISBN: 1457707535; 9781457707537
• ISSN: 2155-6822
• DOI: 10.1109/ICEEI.2011.6021770

Forecast combination, which is a method to combine the result of several predictors, offers a way to improve the forecast result. Several methods have been proposed to combine the forecasting results into single forecast, namely the simple averaging, weighted average on validation performance, or non-parametric combination schemas. Recent literature uses dimensional reduction method for individual prediction and employs ordinary least squares for forecast combination. Other literature combines prediction results from neural networks using dimensional reduction techniques. Thus, those previous combination schemas can be categorized into linear combination methods. This paper aims to explore the use of non-linear combination method to perform the ensemble of individual predictors. We believe that the non-linear combination method may capture the non linear relationship among predictors, thus, may enhance the result of final prediction. The Neural Network (NN), which is widely used in literature for time series tasks, is used to perform such combination. The dataset used in the experiment is the time series data designated for NN5 Competition. The experimental result shows that forecast combination using NN performs better than the best individual predictors, provided that the predictors selected for combination have fairly good performance.