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

TL;DR

This paper introduces MT-GPRMachine, a novel multi-task Gaussian process regression method that leverages spatiotemporal information transformation to improve multi-step-ahead predictions from limited short-term time series data.

Contribution

It proposes a new spatiotemporal information transformation scheme and a multi-task GPR model to enhance prediction accuracy from high-dimensional short-term time series.

Findings

Outperforms existing methods on synthetic datasets

Effective in real-world applications

Accurate multi-step predictions from limited data

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.