IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i11p485-d1508278.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/11/485/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/11/485/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Xiaolei & Lin, Zi, 2021. "Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory," Energy, Elsevier, vol. 227(C).
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
    2. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
    3. Blanka Horvath & Josef Teichmann & Žan Žurič, 2021. "Deep Hedging under Rough Volatility," Risks, MDPI, vol. 9(7), pages 1-20, July.
    4. Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
    5. Yukseltan, E. & Kok, A. & Yucekaya, A. & Bilge, A. & Aktunc, E. Agca & Hekimoglu, M., 2022. "The impact of the COVID-19 pandemic and behavioral restrictions on electricity consumption and the daily demand curve in Turkey," Utilities Policy, Elsevier, vol. 76(C).
    6. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    7. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    8. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
    9. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "How Robust are Limit Order Book Representations under Data Perturbation?," Papers 2110.04752, arXiv.org.
    10. Quintero Gutiérrez, Laura Victoria & García Rendón, John & Gutiérrez Gómez, Alejandro, 2024. "Impact of COVID-19 preventive measures on electricity demand: Evidence from Colombia," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    11. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
    12. Cassetti, Gabriele & Boitier, Baptiste & Elia, Alessia & Le Mouël, Pierre & Gargiulo, Maurizio & Zagamé, Paul & Nikas, Alexandros & Koasidis, Konstantinos & Doukas, Haris & Chiodi, Alessandro, 2023. "The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective," Energy, Elsevier, vol. 263(PC).
    13. Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Papers 2102.01962, arXiv.org.
    14. Nicolas Boursin & Carl Remlinger & Joseph Mikael & Carol Anne Hargreaves, 2022. "Deep Generators on Commodity Markets; application to Deep Hedging," Papers 2205.13942, arXiv.org.
    15. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
    16. Shun Chen & Lingling Guo & Lei Ge, 2024. "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2853-2878, November.
    17. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
    18. Aleksandar Arandjelovi'c & Julia Eisenberg, 2024. "Reinsurance with neural networks," Papers 2408.06168, arXiv.org.
    19. Andrew Lesniewski & Giulio Trigila, 2024. "Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics," Papers 2412.00036, arXiv.org, revised Feb 2025.
    20. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:17:y:2024:i:11:p:485-:d:1508278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.