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

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...

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Published in:Computational economics Vol. 64; no. 1; pp. 181 - 210
Main Authors: Liu, Chao, Gao, Fengfeng, Zhang, Mengwan, Li, Yuanrui, Qian, Cun
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2024
Springer
Springer Nature B.V
Subjects:
Algorithms
Behavioral/Experimental Economics
Clustering
Computer Appl. in Social and Behavioral Sciences
Decomposition
Discrete Wavelet Transform
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Economics and Finance
Evolutionary algorithms
Forecasting
Forecasts and trends
Learning
Math Applications in Computer Science
Neural networks
Operations Research/Decision Theory
Prediction models
Predictions
Securities markets
Short term memory
Stock exchanges
Stock markets
Time series
Wavelet transforms
Weighting
Reference vector-based multi-objective clustering (RVMOC)
Evolutionary algorithm (EA)
Maximal overlap discrete wavelet transform (MODWT)
Attention-based long short-term memory (A-LSTM)
ISSN:0927-7099, 1572-9974
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Summary: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.
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ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-023-10432-0