Spatiotemporal information conversion machine for time-series prediction
This work addresses robust time-series prediction for applications in AI and machine learning, offering a model-free method based on observed data, though it appears incremental as it builds on existing techniques like temporal convolutional networks.
The authors tackled the problem of robust multistep-ahead time-series prediction in nonlinear systems by developing the spatiotemporal information conversion machine (STICM), which combines a spatial-temporal information transformation with temporal convolutional networks to map high-dimensional data to future values, showing superior and robust performance on benchmark and real-world datasets, even with noisy data.
Making predictions in a robust way is a difficult task only based on the observed data of a nonlinear system. In this work, a neural network computing framework, the spatiotemporal information conversion machine (STICM), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. STICM combines the advantages of both the STI equation and the temporal convolutional network, 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 STICM 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 of time-series. The STICM 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 STICM has great potential in practical applications in artificial intelligence (AI) or 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.