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

doi:10.1093/nsr/nwaa025

Paper

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

Published Feb 19, 2020 · Chuan Chen, Rui Li, Lin Shu

National Science Review

32

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2

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Abstract

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 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.

Study Snapshot

The Anticipated Learning Machine (ALM) effectively predicts future dynamics from short-term high-dimensional data, outperforming existing methods and offering a new approach for dynamics-based machine learning.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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

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