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DWE-IL: a new incremental learning algorithm for non-stationary time series prediction via dynamically weighting ensemble learning - 2021

Dwe-Il: A New Incremental Learning Algorithm For Non-Stationary Time Series Prediction Via Dynamically Weighting Ensemble Learning

Research Area:  Machine Learning


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.


Author(s) Name:  Huihui Yu & Qun Dai

Journal name:  Applied Intelligence

Conferrence name:  

Publisher name:  Springer


Volume Information: