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Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
We conduct comprehensive ablation studies to demonstrate the trade-offs between the number of tunable parameters and forecasting performance and assess the impact of varying LoRA matrix ranks on model performance.
Interpretable Vital Sign Forecasting with Model Agnostic Attention Maps
This paper introduces a framework that combines a deep learning model with an attention mechanism that highlights the critical time steps in the forecasting process, thus improving model interpretability and supporting clinical decision-making.
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