EAAI Journal 2026 Journal Article
A short-term water demand forecasting method integrating wavelet stepwise decomposition and spatial-temporal features
- Chenlei Xie
- Jie Wang
- Tao Chen
- Qiansheng Fang
- Shanshou Li
- Xuelei Yang
Accurate short-term water demand forecasting is crucial for the management and scheduling of water distribution systems. However, existing decomposition-based prediction models face two major challenges: prevalent data leakage during global decomposition, which distorts model evaluation, and the inherent shift-variance in methods designed to avoid leakage, resulting in poor forecasting accuracy. To address these issues, this paper proposes an innovative forecasting framework integrating Wavelet stepwise decomposition (WSD) with spatial-temporal features. The core contributions of this work are threefold: First, the proposed WSD method employs a fixed-length sliding window for decomposition, fundamentally eliminating data leakage. Second, correlation analysis is introduced to optimize the selection of the mother wavelet, thereby minimizing errors caused by shift-variance. Third, a hybrid prediction model is constructed, where Extreme gradient boosting (XGBoost) fits the stable trends of low-frequency subseries, and an inverted Transformer (iTransformer) captures the dynamic dependencies within multi-dimensional spatial-temporal features of high-frequency subseries, significantly enhancing their prediction accuracy. Experimental results on a real-world water distribution networks (WDN) demonstrate that the proposed method outperforms benchmark models, including Long short-term memory (LSTM) and graph-based models.