Time series forecasting based on a multidimensional Taylor network model with clustering of dynamic characteristics

Abstract

In view of the difficulty of modelling time-varying nonlinear systems, a multidimensional Taylor network (MTN) model based on dynamic characteristic clustering is proposed to establish an online time series forecasting model. The construction method of a MTN, the definitions of the dynamic characteristics, and their similarity criteria are discussed. Specific steps of the MTN online model, based on dynamic clustering, are given. A practical example is presented to demonstrate how the proposed method works in a real application, and its effectiveness is verified.

Keywords:

• Dynamic characteristics
• clustering
• multidimensional Taylor network
• time series forecasting

Acknowledgements

We thank the Editor-in-Chief, the Associate Editor, the anonymous reviewers, Professor Li Lu, and Elsevier Language Editing Services for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China under Grants 61673112 and 60934008 and the Fundamental Research Funds for the Central Universities of China under Grants 2242017K10003, 2242014K10031, and Shanghai Aerospace Science and Technology Innovation Foundation under Grant SAST2019-020, as well as by the Priority Academic Program Development of Jiangsu Higher Education Institutions.