点击次数：

所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：Appl. Soft Comput. J.

摘要：Time series prediction for higher future horizons is of great importance and has increasingly aroused interest among both scholars and practitioners. Compared to one-step-ahead prediction, multi-step-ahead prediction encounters higher dose of uncertainty arising from various facets, including accumulation of errors and lack of information. Many existing studies draw attention to the former issue, while relatively overlook the latter one. Inspired by this discovery, a new multi-task learning algorithm, called the MultiTL-KELM algorithm for short, is proposed for multi-step-ahead time series prediction in this work, where the long-ago data is utilized to provide more information for the current prediction task. The time-varying quality of time-series data usually gives rise to a wide variability between data over long time span, making it difficult to ensure the assumption of identical distribution. How to make the most of, rather than discard the abundant old data, and transfer more useful knowledge to current prediction is one of the main concerns of our proposed MultiTL-KELM algorithm. Besides, unlike typical iterated or direct strategies, MultiTL-KELM regards predictions of different horizons as different tasks. Knowledge from one task can benefit others, enabling it to explore the relatedness among horizons. Based upon its design scheme, MultiTL-KELM alleviates the accumulation error problem of iterated strategy and the time consuming of direct strategies. The proposed MultiTL-KELM algorithm has been compared with several other state-of-the-art algorithms, and its effectiveness has been numerically confirmed by the experiments we conducted on four synthetic and two real-world benchmark time series datasets. © 2019 Elsevier B.V.

ISSN号：1568-4946

是否译文：否

发表时间：2019-06-01

合写作者：叶锐

通讯作者：戴群