Modeling Combinatorial Evolution in Time Series Prediction.
Year: 2019
Type: preprint
Abstract: Time series modeling aims to capture the intrinsic factors underpinningobserved data and its evolution. However, most existing studies ignore the
evolutionary relations among these factors, which are what cause the
combinatorial evolution of a given time series. In this paper, we propose to
represent time-varying relations among intrinsic factors of time series data by
means of an evolutionary state graph structure. Accordingly, we propose the
Evolutionary Graph Recurrent Networks (EGRN) to learn representations of these
factors, along with the given time series, using a graph neural network
framework. The learned representations can then be applied to time series
classification tasks. From our experiment results, based on six real-world
datasets, it can be seen that our approach clearly outperforms ten
state-of-the-art baseline methods (e.g. +5% in terms of accuracy, and +15% in
terms of F1 on average). In addition, we demonstrate that due to the graph
structure's improved interpretability, our method is also able to explain the
logical causes of the predicted events.
Source: arXiv (Cornell University)
Authors: Wenjie Hu, Yang Yang, Zilong You, Zongtao Liu, Xiang Ren
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2944705240