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DWE-IL: a new incremental learning algorithm for non-stationary time series prediction via dynamically weighting ensemble learning

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Abstract

In this work, an Incremental Learning Algorithm via Dynamically Weighting Ensemble Learning (DWE-IL) is proposed to solve the problem of Non-Stationary Time Series Prediction (NS-TSP). The basic principle of DWE-IL is to track real-time data changes by dynamically establishing and maintaining a knowledge base composed of multiple basic models. It trains the base model for each non-stationary time series subset, and finally combine each base model with dynamically weighting rules. The emphasis of the DWE-IL algorithm lies in the update of data weights and base model weights and the training of the base model. Finally, the experimental results of the DWE-IL algorithm on six non-stationary time series datasets are presented and compared with those of several other excellent algorithms. It can be concluded from the experimental results that the DWE-IL algorithm provides a good solution to the challenges of the NS-TSP tasks and has significantly superior performance over other comparative algorithms.

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Acknowledgments

This work is supported by the National Key R&D Program of China (Grant Nos. 2018YFC2001600, 2018YFC2001602), and the National Natural Science Foundation of China under Grant no. 61473150.

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  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Huihui Yu & Qun Dai

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  1. Huihui Yu
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Correspondence to Qun Dai.

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Yu, H., Dai, Q. DWE-IL: a new incremental learning algorithm for non-stationary time series prediction via dynamically weighting ensemble learning. Appl Intell 52, 174–194 (2022). https://doi.org/10.1007/s10489-021-02385-4

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