  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.
