1D convolutional neural networks for chart pattern classification in financial time series

This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural ne...

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Veröffentlicht in:	The Journal of supercomputing Jg. 78; H. 12; S. 14191 - 14214
Hauptverfasser:	Liu, Liying, Si, Yain-Whar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2022 Springer Nature B.V
Schlagworte:	Artificial neural networks Compilers Computer Science Computer vision Datasets Deep learning Image classification Interpreters Machine learning Object recognition Pattern classification Processor Architectures Programming Languages Support vector machines Time series Chart patterns 1D convolutional neural networks Financial time series Classification
ISSN:	0920-8542, 1573-0484
Online-Zugang:	Volltext
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