Discovering Predictable Latent Factors for Time Series Forecasting

作     者：Hou, Jingyi Dong, Zhen Zhou, Jiayu Liu, Zhijie 

作者机构：The School of Intelligence Science and Technology University of Science and Technology Beijing Beijing100083 China The Institute of Artificial Intelligence University of Science and Technology Beijing Beijing100083 China The Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing100083 China The College of Mathematics and Computer Science Yan’An University Yan’an716000 China The Department of Computer Science and Engineering Michigan State University East LansingMI48823 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Efficiency 

摘      要：Modern temporal modeling methods, such as Transformer and its variants, have demonstrated remarkable capabilities in handling sequential data from specific domains like language and vision. Though achieving high performance with large-scale data, they often have redundant or unexplainable structures. When encountering some real-world datasets with limited observable variables that can be affected by many unknown factors, these methods may struggle to identify meaningful patterns and dependencies inherent in data, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring latent factors implied by the observed temporal data. The inferred factors are used to form multiple predictable and independent signal components that enable not only the reconstruction of future time series for accurate prediction but also sparse relation reasoning for long-term efficiency. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics of latent factors via powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting tasks. Statistical analyses validate the predictability and interpretability of the learned latent factors. Copyright © 2023, The Authors. All rights reserved.