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

Chuan Chen

doi:10.1093/nsr/nwaa025

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

2020-02-19

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DOI：10.1093/nsr/nwaa025

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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 ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning.

Keywords:

dynamics-based machine learning

delay embedding theory

short-term time series prediction

dynamics-based data science

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

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