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

Nazgul Zakiyeva () and Milena Petkovic ()
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Nazgul Zakiyeva: Technische Universität Berlin, Chair of Software and Algorithms for Discrete Optimization
Milena Petkovic: Zuse Institute Berlin, Applied Algorithmic Intelligence Methods Department

Chapter Chapter 54 in Operations Research Proceedings 2023, 2025, pp 423-429 from Springer

Abstract: 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.

Keywords: Functional autoregression; Forecasting; Mixed-integer programming (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-58405-3_54

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DOI: 10.1007/978-3-031-58405-3_54

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