Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time Series Prediction by Multi-task GPR with Spatiotemporal Information
  Transformation   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.

27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an