A time-dependent enhanced support vector machine for time series regression

Abstract

Support Vector Machines (SVMs) are a leading tool in ma- chine learning and have been used with considerable success for the task of time series forecasting. However, a key chal- lenge when using SVMs for time series is the question of how to deeply integrate time elements into the learning process. To address this challenge, we investigated the distribution of errors in the forecasts delivered by standard SVMs. Once we identified the samples that produced the largest errors, we observed their correlation with distribution shifts that occur in the time series. This motivated us to propose a time-dependent loss function which allows the inclusion of the information about the distribution shif..