Leveraging Potentials of Local and Global Models for Water Demand Forecasting †
- This paper examines the effectiveness of local and global models in predicting water demand, employing data from the Battle of Water Demand Forecasting. Utilizing LightGBM models under local, semi-global, and global settings, we analyze the performance of these models across different configurations. The results suggest that inadequately optimized hyperparameters do not always enhance model performance, but well performing hyperparameters can be appropriate for different model types inside the domain of water demand forecasting. Semi-global and global models frequently outperformed local models, underscoring the benefits of contextual information. Our findings indicate that while semi-global approaches offer promising results, extensive tuning and a strategic selection of a time series for modeling are imperative for forecasting accuracy.
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| Author: | Matthias GroßORCiD, Lukas HansORCiD |
|---|---|
| URN: | urn:nbn:de:hbz:832-epub4-27566 |
| DOI: | https://doi.org/10.3390/engproc2024069129 |
| ISSN: | 2673-4591 |
| Parent Title (English): | Engineering Proceedings |
| Publisher: | MDPI |
| Editor: | Stefano Alvisi, Marco Franchini, Valentina Marsili, Filippo Mazzoni |
| Document Type: | Article |
| Language: | English |
| Date of Publication (online): | 2025/04/04 |
| GND-Keyword: | Zeitreihenanalyse |
| Tag: | Global and Local Modeling; LightGBM; Predictive Modeling; Time Series Analysis; Water Demand Forecasting |
| Volume: | 69 |
| Issue: | 1 |
| Page Number: | 4 |
| Institutes: | Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Data Science, Engineering, and Analytics |
| Dewey Decimal Classification: | 000 Allgemeines, Informatik, Informationswissenschaft |
| Open Access: | Open Access |
| DeepGreen: | DeepGreen |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |