A dynamic predictor selection algorithm for predicting stock market movement

Although training a deep network with financial time series is not hard, the important issue is, how much the prediction for the truly new data can be trusted with a trained network. In this study, we propose a dynamic predictor selection algorithm (DPSA) that dynamically evaluates and selects the p...

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Vydané v:	Expert systems with applications Ročník 186; s. 115836
Hlavní autori:	Dong, Shuting, Wang, Jianxin, Luo, Hongze, Wang, Haodong, Wu, Fang-Xiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 30.12.2021 Elsevier BV
Predmet:	Algorithms Clustering ConvLSTM Deep learning Dynamic prediction Financial time series Improved KFCM algorithm Kernels Machine learning Performance prediction Prediction models Stock exchanges Time series Time-weighted Training Deep learning Time-weighted Improved KFCM algorithm Financial time series ConvLSTM Dynamic prediction
ISSN:	0957-4174, 1873-6793
On-line prístup:	Získať plný text
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