10193 2025 eng reportzib 0 -- -- -- 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. Econometrics and Statistics 10.1016/j.ecosta.2025.10.001 urn:nbn:de:0297-zib-101936 yes accepted for publication 07.10.2025 publish Published in Econometrics and Statistics https://doi.org/10.1016/j.ecosta.2025.10.001 false true Nazgul Zakiyeva Nazgul Zakiyeva Milena Petkovic 25-17 Petkovic, Milena Applied Algorithmic Intelligence Methods MODAL-EnergyLab Zakiyeva, Nazgul Applied Optimization https://opus4.kobv.de/opus4-zib/files/10193/FNAR.pdf