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Financial Time Series Forecasting using CNN and Transformer

Author

Listed:
  • Zhen Zeng
  • Rachneet Kaur
  • Suchetha Siddagangappa
  • Saba Rahimi
  • Tucker Balch
  • Manuela Veloso

Abstract

Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are good at capturing local patterns for modeling short-term dependencies. However, CNNs cannot learn long-term dependencies due to the limited receptive field. Transformers on the other hand are capable of learning global context and long-term dependencies. In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series, and forecast if the price would go up, down or remain the same (flat) in the future. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.

Suggested Citation

  • Zhen Zeng & Rachneet Kaur & Suchetha Siddagangappa & Saba Rahimi & Tucker Balch & Manuela Veloso, 2023. "Financial Time Series Forecasting using CNN and Transformer," Papers 2304.04912, arXiv.org.
    • Handle: RePEc:arx:papers:2304.04912
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    References listed on IDEAS

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    3. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    4. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    5. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    6. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    7. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
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    Cited by:

    1. Darko B. Vuković & Sonja D. Radenković & Ivana Simeunović & Vyacheslav Zinovev & Milan Radovanović, 2024. "Predictive Patterns and Market Efficiency: A Deep Learning Approach to Financial Time Series Forecasting," Mathematics, MDPI, vol. 12(19), pages 1-26, September.
    2. Maheronnaghsh, Mohammad Javad & Gheidi, Mohammad Mahdi & Fazli, MohammadAmin, 2023. "Machine Learning Methods in Algorithmic Trading: An Experimental Evaluation of Supervised Learning Techniques for Stock Price," OSF Preprints dzp26, Center for Open Science.

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