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

• 作者: Chengming Zhang Chuan Chen Huanfei Ma Jining Wang Kazuyuki Aihara Lin Shu Luonan Chen Rui Li Zhiyu He
• 来源期刊: 国家科学评论（英文版）
• 关键词: dynamics-based machine learning delay embedding theory short-term time series prediction dynamics-based data science
• 年，卷（期）: 2020,（6）
• 页码范围: 1079-1091
• 页数: 13页
• 语种: 英文
• DOI: 10.1093/nsr/nwaa025

摘要

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.