Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting

Chao Liu, Fengfeng Gao (), Mengwan Zhang, Yuanrui Li and Cun Qian
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Chao Liu: Beijing University of Technology
Fengfeng Gao: Beijing University of Technology
Mengwan Zhang: Beijing University of Technology
Yuanrui Li: Beijing University of Technology
Cun Qian: Beijing University of Technology

Computational Economics, 2024, vol. 64, issue 1, No 8, 210 pages

Abstract: Abstract This paper integrates the maximal overlap discrete wavelet transform (MODWT), long and short-term memory neural network (EA-LSTM) of evolutionary attention mechanism and reference vector based clustering algorithm (RVMOC) and proposes a new prediction method of the stock market return rate, which is referred to as the stock market return rate prediction method based on MODWT-EA-LSTM-LSTM-RVMOC. This method uses a clustering strategy based on a reference vector to extend decomposition-integrated learning to nonlinear integrated weighted learning based on local data feature weighting, overcomes the deficiency of the integrated learning stage in the decomposition-integration method, and effectively solves the problem of artificial experience setting of the objective function weight coefficient and clustering accuracy in existing cluster-integrated learning. The empirical results show that compared with the single model and decomposition-integration learning model, the MODWT-EA-LSTM-RVMOC algorithm is better than other models in both prediction error and prediction hit rate. The results also indicate that the RVMOC clustering algorithm can effectively improve the prediction performance of the decomposition-integration model.

Keywords: Attention-based long short-term memory (A-LSTM); Evolutionary algorithm (EA); Reference vector-based multi-objective clustering (RVMOC); Maximal overlap discrete wavelet transform (MODWT) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10614-023-10432-0

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