A novel active multi-source transfer learning algorithm for time series forecasting

Abstract

In Time Series Forecasting (TSF), researchers usually assume that there is enough training data can be obtained, with the old a`nd new data satisfying the same distribution. However, time series data always produces some time-varying characteristics over time, which will lead to relatively large differences between old and new data. As we all know, single-source TSF Transfer Learning (TL) faces the problem of negative transfer. Addressing this issue, this paper proposes a new Multi-Source TL algorithm, abbreviated as the MultiSrcTL algorithm, and a novel Active Multi-Source Transfer Learning, abbreviated as the AcMultiSrcTL algorithm, with the latter one integrating Multi-Source TL with Active Learning (AL), and taking the former one as its sub-algorithm. We introduce domain adaptation theory into this work, and analyze the expected target risk of TSF under the multi-source setting, accordingly. For the development of MultiSrcTL, we make full use of source similarity and domain dependability, using the Maximum Mean Discrepancy statistical indicator to measure the similarity between domains, so as to promote better transfer. A domain relation matrix is constructed to describe the relationship between source domains, so that the source-source and source-target relations are adequately considered. In the design of AcMultiSrcTL, Kullback-Leibler divergence is used to measure the similarity of related indicators to select the appropriate source domain. The uncertainty sampling method and the distribution match weighting technique are integrated, obtaining a new sample selection scheme. The empirical results on six benchmark datasets demonstrate the applicability and effectiveness of the two proposed algorithms for multi-source TSF TL.