Large-scale functional network time series model solved with mathematical programming approach
Zakiyeva N. Petkovic M.
2025Elsevier B.V.
Econometrics and Statistics
2025
A functional network autoregressive model is proposed for studying large-scale network time series observed at high temporal resolution. The model incorporates high-dimensional curves to capture both serial and cross-sectional dependence in large-scale network functional time series. Estimation of the model is approached using a Mixed Integer Optimization method. Simulation studies confirm the consistency of parameter and adjacency matrix estimation. The method is applied to data from a real-life natural gas supply network. Compared to alternative prediction models, the proposed model delivers more accurate day-ahead hourly out-of-sample forecasts of the gas inflows and outflows at most gas nodes.
Best subset selection , Functional time series , High-dimensionality , Mixed integer optimization , Network modeling
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Institute of Mathematics and Mathematical Modeling, 125 Pushkin str, Almaty, 050010, Kazakhstan
Technische Universität Berlin, Chair of Software and Algorithms for Discrete Optimization, Straße des 17. Juni 135, Berlin, 10623, Germany
Leibniz-Institut für Kristallzüchtung, Max-Born-Straße 2, Berlin, 12489, Germany
Zuse Institute Berlin, Applied Algorithmic Intelligence Methods Department, Takustraße 7, Berlin, 14195, Germany
Institute of Mathematics and Mathematical Modeling
Technische Universität Berlin
Leibniz-Institut für Kristallzüchtung
Zuse Institute Berlin
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