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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Vydané v:The Journal of supercomputing Ročník 78; číslo 12; s. 14191 - 14214
Hlavní autori: Liu, Liying, Si, Yain-Whar
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.08.2022
Springer Nature B.V
Predmet:
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
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Shrnutí: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 networks (2D CNNs). In this paper, we describe the design and implementation of one-dimensional convolutional neural networks (1D CNNs) for the classification of chart patterns from financial time series. The proposed 1D CNN model is compared against support vector machine, extreme learning machine, long short-term memory, rule-based and dynamic time warping. Experimental results on synthetic datasets reveal that the accuracy of 1D CNN is highest among all the methods evaluated. Results on real datasets also reveal that chart patterns identified by 1D CNN are also the most recognized instances when they are compared to those classified by other methods.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04431-5