EnsPKDE&IncLKDE: a hybrid time series prediction algorithm integrating dynamic ensemble pruning, incremental learning, and kernel density estimation - Zhu, Gangliang; Dai, Qun (Applied intelligence (Dordrecht, Netherlands), 2021)
Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on other samples. Blindly underestimating the power of specific predictors is unreasonable. Choosing the best predictor set for each query sample is exactly what dynamic ensemble pruning techniques address. This paper proposes a hybrid Time Series Prediction (TSP) algorithm to implement one-step-ahead prediction task, integrating Dynamic Ensemble Pruning (DEP), Incremental Learning (IL), and Kernel Density Estimation (KDE), abbreviated as the EnsP KDE &IncL KDE algorithm. It dynamically selects proper predictor sets based on the kernel density distribution of all base learners’ prediction values. Due to the characteristic of TSP problems that samples arrive in chronological order, the idea of IL is naturally introduced into EnsP KDE &IncL KDE , while DEP is a common technology to address the concept drift issue inherent in IL. The algorithm is divided into three subprocesses: 1) Overproduction, which generates the original ensemble learning system; 2) Dynamic Ensemble Pruning (DEP), achieved by one subalgorithm called EnsP KDE ; 3) Incremental Learning (IL), realized by one subalgorithm termed IncL KDE . Benefited from the advantages of integrating Dynamic Ensemble Pruning scheme, Incremental Learning paradigm and Kernel Density Estimation, in the experimental results, EnsP KDE &IncL KDE demonstrates superior prediction performance to several other state-of-the-art algorithms in fulfilling time series forecasting tasks.