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Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting

Divij Gupta, Anubhav Bhatti, Suraj Parmar, Chen Dan, Yuwei Liu, Bingjie Shen, San Lee

TL;DR

Low-Rank Adaptation’s fine-tuning potential for forecasting the vital signs of sepsis patients in intensive care units (ICUs) is demonstrated, emphasizing the models’ adaptability to previously unseen, out-of-domain modalities.

Abstract

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.

Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting

TL;DR

Low-Rank Adaptation’s fine-tuning potential for forecasting the vital signs of sepsis patients in intensive care units (ICUs) is demonstrated, emphasizing the models’ adaptability to previously unseen, out-of-domain modalities.

Abstract

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.
Paper Structure (6 sections, 3 equations, 3 figures, 1 table)

This paper contains 6 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Experiment Setup
  5. Results and Discussion
  6. Conclusion and Future Work

Figures (3)

  • Figure 1: The primary components of our method: a) A transformer-based TSFM for learning rich feature representations from time series data; b) Multi-Head Self-Attention attention; c) The Low-Rank Adaptation (LoRA) mechanism.
  • Figure 2: Performance across varying model parameter settings. Marker size indicates the number of fine-tuned parameters. Values are scaled appropriately for clarity.
  • Figure 3: Performance across varying matrix ranks during LoRA fine-tuning for different models.