Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning

TL;DR

This paper introduces LTSV, a lightweight, scalable method for valuing time series data on foundation models by using in-context finetuning to estimate data contribution efficiently while preserving temporal dependencies.

Contribution

LTSV leverages in-context finetuning to provide a scalable, effective data valuation method for time series foundation models, addressing limitations of traditional influence-based approaches.

Findings

LTSV achieves reliable data valuation across multiple datasets.

LTSV maintains computational efficiency for large models.

Temporal block aggregation improves temporal dependency capture.

Abstract

Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the quality of time series data is crucial to TSFM performance, rendering an accurate and efficient data valuation of time series for TSFMs indispensable. However, traditional data valuation methods, such as influence functions, face severe computational bottlenecks due to their poor scalability with growing TSFM model sizes and often fail to preserve temporal dependencies. In this paper, we propose LTSV, a Lightweight Time Series Valuation on TSFMS via in-context finetuning. Grounded in the theoretical evidence that in-context finetuning approximates the influence function, LTSV estimates a sample's contribution by measuring the change in context loss after in-context finetuning, leveraging the strong generalization capabilities of TSFMs to produce robust and transferable data valuations. To capture temporal dependencies, we introduce temporal block aggregation, which integrates per-block influence scores across overlapping time windows. Experiments across multiple time series datasets and models demonstrate that LTSV consistently provides reliable and strong valuation performance, while maintaining manageable computational requirements. Our results suggest that in-context finetuning on time series foundation models provides a practical and effective bridge between data attribution and model generalization in time series learning.