Kernel general loss algorithm based on evolving participatory learning for online time series prediction

In this paper, a dynamic model for online time series prediction is proposed, namely kernel general loss algorithm based on evolving participatory learning (EPL-KGLA). The algorithm can develop its structure and parameters autonomously in response to complex environments, capturing the dynamic changes of time series and achieving accurate prediction. Specifically, EPL based on evolving fuzzy systems is employed in recursive clustering to fully utilize useful information in data streams and generate/prune structures to ensure compactness and reduce computational burden. Then, the general loss function is combined with online kernel learning to propose KGLA for updating consequent parameters in real-time, capturing the dynamic features of data streams and avoiding the negative effects of large anomalies or complex noise on model performance, thereby improving prediction accuracy. Finally, simulation experiments on a benchmark dataset and two real-world datasets are verified the robustness of EPL-KGLA.

Automated summary

This paper proposes a dynamic model called kernel general loss algorithm based on evolving participatory learning (EPL-KGLA) for online time series prediction. The algorithm can autonomously adjust its structure and parameters to adapt to complex environments, accurately capturing the dynamic changes of time series. EPL based on evolving fuzzy systems is used in recursive clustering to utilize useful information in data streams and generate/prune structures to ensure compactness and reduce computational burden. The general loss function is combined with online kernel learning to update consequent parameters in real-time, capturing the dynamic features of data streams and improving prediction accuracy by avoiding the negative effects of anomalies or noise.