Large-scale functional network time series model solved with mathematical programming approach
Please always quote using this URN: urn:nbn:de:0297-zib-101936
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- 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.
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| Author: | Nazgul Zakiyeva, Milena Petkovic |
|---|---|
| Document Type: | ZIB-Report |
| Parent Title (English): | Econometrics and Statistics |
| Year of first publication: | 2025 |
| Series (Serial Number): | ZIB-Report (25-17) |
| Published in: | Published in Econometrics and Statistics https://doi.org/10.1016/j.ecosta.2025.10.001 |
| DOI: | https://doi.org/10.1016/j.ecosta.2025.10.001 |
