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This paper formalizes the 'Granularity Paradox' in time-series forecasting, showing that finer temporal disaggregation improves in-sample fit but degrades out-of-sample accuracy due to recursive error propagation, and benchmarks multiple models across granularities.
This paper introduces DEPOOL, a controlled benchmark evaluating six temporal aggregation architectures across six frozen speech backbones for depression detection in dyadic interactions, finding that many configurations collapse into single-class predictions and that robustness should be a key criterion.
Proposes ReTAMamba, a method using reliability-aware temporal aggregation with Mamba for irregular clinical time series prediction, achieving significant AUPRC gains on MIMIC-IV, eICU, and PhysioNet 2012.