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Image Processing Tools for Financial Time Series Classification

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  • Bairui Du
  • Delmiro Fernandez-Reyes
  • Paolo Barucca

Abstract

The application of deep learning to time series forecasting is one of the major challenges in present machine learning. We propose a novel methodology that combines machine learning and image processing methods to define and predict market states with intraday financial data. A wavelet transform is applied to the log-return of stock prices for both image extraction and denoising. A convolutional neural network then extracts patterns from denoised wavelet images to classify daily time series, i.e. a market state is associated with the binary prediction of the daily close price movement based on the wavelet image constructed from the price changes in the first hours of the day. This method overcomes the low signal-to-noise ratio problem in financial time series and gets a competitive prediction accuracy of the market states 'Up' and 'Down' of financial data as tested on the S&P 500.

Suggested Citation

  • Bairui Du & Delmiro Fernandez-Reyes & Paolo Barucca, 2020. "Image Processing Tools for Financial Time Series Classification," Papers 2008.06042, arXiv.org, revised Aug 2020.
    • Handle: RePEc:arx:papers:2008.06042
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    References listed on IDEAS

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    1. Pier Francesco Procacci & Tomaso Aste, 2019. "Forecasting market states," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1491-1498, September.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    3. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 251-256.
    4. Pier Francesco Procacci & Tomaso Aste, 2018. "Forecasting market states," Papers 1807.05836, arXiv.org, revised May 2019.
    5. Jean-Philippe Bouchaud & Marc Mezard & Marc Potters, 2002. "Statistical properties of stock order books: empirical results and models," Science & Finance (CFM) working paper archive 0203511, Science & Finance, Capital Fund Management.
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    Cited by:

    1. Zhen Zeng & Tucker Balch & Manuela Veloso, 2021. "Deep Video Prediction for Time Series Forecasting," Papers 2102.12061, arXiv.org, revised Nov 2021.
    2. Srijan Sood & Zhen Zeng & Naftali Cohen & Tucker Balch & Manuela Veloso, 2020. "Visual Time Series Forecasting: An Image-driven Approach," Papers 2011.09052, arXiv.org, revised Nov 2021.

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