Time-Series Foundation Models for Short-Term Water Demand Forecasting

Short-Term Water Demand Forecasting (STWDF) helps water-system operators schedule pumps around volatile electricity prices, but many existing forecasting methods require use-case-specific training data and maintenance effort that limit their practical deployment. We investigate whether pretrained Time-Series Foundation Models (TSFMs) can forecast short-term water demand without task-specific training. We evaluate ten openly available TSFMs in a zero-shot univariate setting on 26 real-world water demand datasets and compare against baselines. Seven of the ten TSFMs achieve lower mean Weighted Mean Absolute Percentage Error (wMAPE) than Seasonal Naive. Chronos-2 has a mean wMAPE of 17.3%, compared with 23.8% for Seasonal Naive, although several leading TSFMs are practically equivalent. Longer contexts and shorter horizons generally improve forecast quality. Weather and calendar covariates yield no consistent improvement in our setup, while fine-tuning can improve performance. In a comparison with 31 use-case-specific STWDF models, two TSFMs rank in the top three. Several pretrained TSFMs provide strong zero-shot baselines for STWDF in the evaluated setting. All code, data, and experimental results are publicly available on GitHub.