Predicting time series by data-driven spatiotemporal information transformation

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 multistep-ahead manner. However, a high-dimensional short-term time series still contains rich dynamical information and is increasingly available in many fields. In this work, we exploit a spatiotemporal information (STI) scheme that transforms high-dimensional/spatial information into temporal information and develop a new method called multitask Gaussian process regression machine (MT-GPRM) to achieve accurate predictions from short-term time series. We first construct a specific multitask GPR comprising multiple linked STI mappings to transform high-dimensional/spatial information into temporal/dynamical information of any given target variable and then make multistep-ahead predictions of the target variable by solving those STI mappings. The multistep-ahead prediction results on various synthetic and real-world datasets show that MT-GPRM outperforms other existing approaches.