Nonlinearly weighted multiple kernel learning for time series forecasting

Abstract

Machine Learning methods such as Neural Network (NN) and Support Vector Regression (SVR) have been studied extensively for time series forecasting. Multiple Kernel Learning (MKL) which utilizes SVR as the predictor is yet another recent approaches to choose suitable kernels from a given pool of kernels by means of a linear combination of some base kernels. However, some literatures suggest that this linear combination of kernels cannot consistently outperform either the uniform combination of base kernels or simply the best single kernel. Hence, in this paper, other combination method is devised, namely the squared combination of base kernels, which gives more weight on suitable kernels and vice versa. We use time series data having various length, pattern and horizons, namely the 111 time series from NN3 competition, 3003 of M3 competition, 1001 of Ml competition and reduced 111 of Ml competition. Our experimental results indicate that our new forecasting approaches using squared combination of Multiple Kernel Learning (MKL) may perform well compared to the other methods on the same dataset.