A weighted LS-SVM based learning system for time series forecasting

Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. Statistical approaches have been extensively adopted for time series forecasting in the past decades. Recently, machine learning techniques have drawn attention and useful forecasting systems based on these techniques have been developed. In this paper, we propose a weighted Least Squares Support Vector Machine (LS-SVM) based approach for time series forecasting. Given a forecasting sequence, a suitable set of training patterns are extracted from the historical data by employing the concepts of k-nearest neighbors and mutual information. Based on the training patterns, a modified LS-SVM is developed to derive a forecasting model which can then be used for forecasting. Our proposed approach has several advantages. It can produce adaptive forecasting models. It works for univariate and multivariate cases. It also works for one-step as well as multi-step forecasting. A number of experiments are conducted to demonstrate the effectiveness of the proposed approach for time series forecasting.