1 research outputs found
Spatiotemporal convolutional network for time-series prediction and causal inference
Making predictions in a robust way is not easy for nonlinear systems. In this
work, a neural network computing framework, i.e., a spatiotemporal
convolutional network (STCN), was developed to efficiently and accurately
render a multistep-ahead prediction of a time series by employing a
spatial-temporal information (STI) transformation. The STCN combines the
advantages of both the temporal convolutional network (TCN) and the STI
equation, which maps the high-dimensional/spatial data to the future temporal
values of a target variable, thus naturally providing the prediction of the
target variable. From the observed variables, the STCN also infers the causal
factors of the target variable in the sense of Granger causality, which are in
turn selected as effective spatial information to improve the prediction
robustness. The STCN was successfully applied to both benchmark systems and
real-world datasets, all of which show superior and robust performance in
multistep-ahead prediction, even when the data were perturbed by noise. From
both theoretical and computational viewpoints, the STCN has great potential in
practical applications in artificial intelligence (AI) or machine learning
fields as a model-free method based only on the observed data, and also opens a
new way to explore the observed high-dimensional data in a dynamical manner for
machine learning.Comment: 23 pages, 6 figure