Predicting future dynamics from short-term time series using an Anticipated Learning Machine.

ABSTRACT: Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of

SUBMITTER: Chen C 

PROVIDER: S-EPMC8288952 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature