A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health

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A Comparative Study of Deep Learning Architectures for Multi-Horizon Behavioural Forecasting for Mobile Health

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Wearables generate a continuous stream of behavioral data — steps, screen time, sleep — that could power truly proactive health interventions, but it's been unclear which AI architectures best handle these signals across diverse populations and time horizons. This study benchmarks six deep learning models plus two foundation models across 800+ participants, tracking forecast accuracy out to eight days. Key findings: no single architecture dominates; the foundation model TimesFM matches trained models zero-shot; and personalized fine-tuning cuts error by 16–60%, with sleep benefiting most. Applications include preventive health apps, mental health monitoring, chronic disease management platforms, and research tools for digital health studies where population-level and individual-level accuracy both matter. Authors: Pavlos Nicolaou, Kleanthis Malialis, Artemis Kontou, Panayiotis Kolios Paper: https://arxiv.org/abs/2606.14604v1

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