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High-Dimensional High-Frequency Time Series Prediction with a Mixed Integer Optimisation Method

In: Operations Research Proceedings 2023

Author

Listed:
  • Nazgul Zakiyeva

    (Technische Universität Berlin, Chair of Software and Algorithms for Discrete Optimization)

  • Milena Petkovic

    (Zuse Institute Berlin, Applied Algorithmic Intelligence Methods Department)

Abstract

We study a functional autoregressive model for high-frequency time series. We approach the estimation of the proposed model using a Mixed Integer Optimisation method. The proposed model captures serial dependence in the functional time series by including high-dimensional curves. We illustrate our methodology on large-scale natural gas network data. Our model provides more accurate day-ahead hourly out-of-sample forecast of the gas in and out-flows compared to alternative prediction models.

Suggested Citation

  • Nazgul Zakiyeva & Milena Petkovic, 2025. "High-Dimensional High-Frequency Time Series Prediction with a Mixed Integer Optimisation Method," Lecture Notes in Operations Research, in: Guido Voigt & Malte Fliedner & Knut Haase & Wolfgang Brüggemann & Kai Hoberg & Joern Meissner (ed.), Operations Research Proceedings 2023, chapter 0, pages 423-429, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-58405-3_54
    DOI: 10.1007/978-3-031-58405-3_54
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