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UID:news2067@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20260901T140019
DTSTART;TZID=Europe/Zurich:20260918T110000
SUMMARY:Seminar in Numerical Analysis: Nazgul Zakiyeva (Kanazawa University
 )
DESCRIPTION:A functional network autoregressive model is proposed for study
 ing 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. 
 We estimate the model using a Mixed Integer Optimization method. Simulatio
 n studies confirm the consistency of parameter and adjacency matrix estima
 tion. The method is applied to data from a real-life natural gas supply ne
 twork. Compared to alternative prediction models\, the proposed model deli
 vers more accurate day-ahead hourly out-of-sample forecasts of the gas inf
 lows and outflows at most gas nodes.\\r\\nFor further information about th
 e seminar\, please visit this webpage [https://dmi.unibas.ch/de/forschung/
 mathematik/seminar-in-numerical-analysis/].
X-ALT-DESC:<p>A functional network autoregressive model is proposed for stu
 dying large-scale network time series observed at high temporal resolution
 . The model incorporates high-dimensional curves to capture both serial an
 d cross-sectional dependence in large-scale network functional time series
 . We estimate the model using a Mixed Integer Optimization method. Simulat
 ion studies confirm the consistency of parameter and adjacency matrix esti
 mation. The method is applied to data from a real-life natural gas supply 
 network. Compared to alternative prediction models\, the proposed model de
 livers more accurate day-ahead hourly out-of-sample forecasts of the gas i
 nflows and outflows at most gas nodes.</p>\n<p>For further information abo
 ut the seminar\, please visit this <a href="https://dmi.unibas.ch/de/forsc
 hung/mathematik/seminar-in-numerical-analysis/">webpage</a>.</p>
DTEND;TZID=Europe/Zurich:20260918T123000
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