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[2509.06974] Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model
Summary
This paper presents a novel individualized adaptive spatial-temporal model for predicting sleep quality, demonstrating superior performance over existing forecasting methods.
Why It Matters
Sleep quality is crucial for overall well-being, and this research offers a reliable forecasting tool that can enhance preventive healthcare strategies. The model's explainability and adaptability make it particularly valuable for personalized health interventions using data from wearable devices.
Key Takeaways
- Introduces a two-stage adaptive spatial-temporal model for sleep forecasting.
- Achieves superior predictive performance compared to traditional methods like LSTM and Informer.
- Utilizes channel attention and bidirectional LSTM for enhanced feature extraction.
- Demonstrates practical utility for real-world applications with commercial wearables.
- Includes an explainability analysis to understand feature influences on sleep quality.
Computer Science > Machine Learning arXiv:2509.06974 (cs) [Submitted on 28 Aug 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model Authors:Xueyi Wang, Claudine J. C. Lamoth, Elisabeth Wilhelm View a PDF of the paper titled Individualized and Interpretable Sleep Forecasting via a Two-Stage Adaptive Spatial-Temporal Model, by Xueyi Wang and 1 other authors View PDF HTML (experimental) Abstract:Sleep quality impacts well-being. Therefore, healthcare providers and individuals need accessible and reliable forecasting tools for preventive interventions. This paper introduces an interpretable, individualized adaptive spatial-temporal model for predicting sleep quality. We designed a hierarchical architecture, consisting of parallel 1D convolutions with varying kernel sizes and dilated convolution, which extracts multi-resolution temporal patterns-short kernels capture rapid physiological changes, while larger kernels and dilation model slower trends. The extracted features are then refined through channel attention, which learns to emphasize the most predictive variables for each individual, followed by bidirectional LSTM and self-attention that jointly model both local sequential dynamics and global temporal dependencies. Finally, a two-stage adaptation strategy ensures the learned representations transfer effectively to new users. We conducted various experiments with fi...