  Low-Rank Adaptation (LoRA) is a widely used technique for fine-tuning large
pre-trained or foundational models across different modalities and tasks.
However, its application to time series data, particularly within foundational
models, remains underexplored. This paper examines the impact of LoRA on
contemporary time series foundational models: Lag-Llama, MOIRAI, and Chronos.
We demonstrate LoRA's fine-tuning potential for forecasting the vital signs of
sepsis patients in intensive care units (ICUs), emphasizing the models'
adaptability to previously unseen, out-of-domain modalities. Integrating LoRA
aims to enhance forecasting performance while reducing inefficiencies
associated with fine-tuning large models on limited domain-specific data. Our
experiments show that LoRA fine-tuning of time series foundational models
significantly improves forecasting, achieving results comparable to
state-of-the-art models trained from scratch on similar modalities. 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.
