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arXiv Machine Learning By Ragamayi Puli, Shunya Nagashima

Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction

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The paper introduces ROOSTER, a shared relative‑alignment module that learns how to map a condition sequence to a target sequence for both PPG‑to‑vital‑sign reconstruction and long‑horizon multivariate time‑series forecasting. ROOSTER uses a learnable periodic‑comb bias over the target‑condition offset, allowing it to discover identity alignment or seasonal lags and report the chosen correspondence. Experiments show that ROOSTER outperforms existing baselines on heart‑rate and respiratory‑rate reconstruction from wrist‑worn photoplethysmograms, and achieves the lowest horizon‑averaged MSE on four forecasting benchmarks, outperforming the underlying forecasting model in most dataset‑horizon settings.

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