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arXiv · 2204.12085

Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation

Peng Tao·Xiaohu Hao·Jie Cheng·Luonan Chen
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Abstract

Making an accurate prediction of an unknown system only from a short-term time series is difficult due to the lack of sufficient information, especially in a multi-step-ahead manner. However, a high-dimensional short-term time series contains rich dynamical information, and also becomes increasingly available in many fields. In this work, by exploiting spatiotemporal information (STI) transformation scheme that transforms such high-dimensional/spatial information to temporal information, we developed a new method called MT-GPRMachine to achieve accurate prediction from a short-term time series. Specifically, we first construct a specific multi-task GPR which is multiple linked STI mappings to transform high dimensional/spatial information into temporal/dynamical information of any given target variable, and then makes multi step-ahead prediction of the target variable by solving those STI mappings. The multi-step-ahead prediction results on various synthetic and real-world datasets clearly validated that MT-GPRMachine outperformed other existing approaches.

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Peng Tao, Xiaohu Hao, Jie Cheng, Luonan Chen. 2022-04-26. Time Series Prediction by Multi-task GPR with Spatiotemporal Information Transformation. https://arxiv.org/abs/2204.12085

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