Integrating Multi-Source Transfer Learning, Active Learning and Metric Learning paradigms for Time Series Prediction

Abstract

Traditional Time Series Prediction (TSP) algorithms assume that the training and testing data follow the same distribution and a large amount of data can be obtained. However, the time-varying nature of time series data is a very difficult problem, which will lead to the inconsistent distribution between new data and old data. In TSP problem scenario, how to transfer knowledge in a relatively long-time span effectively is a serious challenge. To address this issue, in this work, a novel Multi-Source Active Metric Transfer Learning (MS-AMTL) algorithm, integrating Multi-Source Transfer Learning, Active Learning, and Metric Learning paradigms, is designed to make the most of, instead of throw away directly, the long-ago data, with effect, along with its precise mathematic derivation. The setting of multi-source domains can make the dependability of domains and the similarity of sources be fully exerted, reducing the impact of negative transfer. The Active Learning (AL) module in MS-AMTL is designed to select the most representative instances, so that dependability of the domains can be ensured and information redundancy can be reduced. The Metric Learning (ML) module in MS-AMTL is developed to measure the similarity between instances and then the similarity of sources, more effectively. Extensive experiments on one artificial dataset, three real-world datasets and four financial datasets proved the superiority of our proposed algorithm in transfer learning and prediction performance in comparison with several other state-of-the-art algorithms.