A dynamic predictor selection algorithm for predicting stock market movement
作者：
Highlights：
• A dynamic predictor selection model for financial prediction is proposed.
• The model organizes the historical samples according to their relevance.
• A clustering algorithm is proposed to improve prediction performance.
• The model needs a few iterations to differentiate each of the predictor.
• The training time of this model is greatly shorter than in deep learning methods.
摘要
•A dynamic predictor selection model for financial prediction is proposed.•The model organizes the historical samples according to their relevance.•A clustering algorithm is proposed to improve prediction performance.•The model needs a few iterations to differentiate each of the predictor.•The training time of this model is greatly shorter than in deep learning methods.
论文关键词：Financial time series,Improved KFCM algorithm,Dynamic prediction,Time-weighted,Deep learning,ConvLSTM
论文评审过程：Received 24 February 2020, Revised 19 August 2021, Accepted 29 August 2021, Available online 5 September 2021, Version of Record 9 September 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115836
