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

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Abstract

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.

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Notes

  1. finance.yahoo.com

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Funding

This research was funded by the University of Macau (File no. MYRG2019-00136-FST).

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  1. Department of Computer and Information Science, University of Macau, Macau, China

    Liying Liu & Yain-Whar Si

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  1. Liying Liu
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  2. Yain-Whar Si
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Correspondence to Yain-Whar Si.

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Liu, L., Si, YW. 1D convolutional neural networks for chart pattern classification in financial time series. J Supercomput 78, 14191–14214 (2022). https://doi.org/10.1007/s11227-022-04431-5

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