Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation.

ABSTRACT: We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network (ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short-term high-dimensional time series. Different from traditional reservoir computing whose reservoir is an external dynamical system irrelevant to the target system, ARNN directly transforms the observed high-dimensional dynamics as its reservoir, which maps the high-dimensional/spatial data to the future temporal values of a target variable based on our spatiotemporal information (STI) transformation. Thus, the multi-step prediction of the target variable is achieved in an accurate and computationally efficient manner. ARNN is successfully applied to both representative models and real-world datasets, all of which show satisf

SUBMITTER: Chen P 

PROVIDER: S-EPMC7486927 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature