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Optimizing Multivariate Time Series Forecasting with Data Augmentation

Author

Listed:
  • Seyed Sina Aria

    (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran)

  • Seyed Hossein Iranmanesh

    (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran)

  • Hossein Hassani

    (School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran)

Abstract

The convergence of data mining and deep learning has become an invaluable tool for gaining insights into evolving events and trends. However, a persistent challenge in utilizing these techniques for forecasting lies in the limited access to comprehensive, error-free data. This challenge is particularly pronounced in financial time series datasets, which are known for their volatility. To address this issue, a novel approach to data augmentation has been introduced, specifically tailored for financial time series forecasting. This approach leverages the power of Generative Adversarial Networks to generate synthetic data that replicate the distribution of authentic data. By integrating synthetic data with real data, the proposed approach significantly improves forecasting accuracy. Tests with real datasets have proven that this method offers a marked improvement over models that rely only on real data.

Suggested Citation

  • Seyed Sina Aria & Seyed Hossein Iranmanesh & Hossein Hassani, 2024. "Optimizing Multivariate Time Series Forecasting with Data Augmentation," JRFM, MDPI, vol. 17(11), pages 1-19, October.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:11:p:485-:d:1508278
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    References listed on IDEAS

    as
    1. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
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