Unified Transferability Metrics for Time Series Foundation Models

Weiyang Zhang, Xinyang Chen, Xiucheng Li, Kehai Chen, Weili Guan, Liqiang Nie

Time Series Analysis; Transferability Estimation; Pre-trained Model

Abstract

With the increasing number of time series pre-trained models, designing transferability evaluation metrics for time series has become an urgent problem to address. While transferability evaluation has been extensively studied in computer vision, we aim to address a critical gap by developing tailored metrics for time series analysis.

In this paper, we introduce TEMPLATE, a transferability estimation framework specifically tailored for versatile time series analysis, comprising three complementary metrics:

Dependency Learning Score quantifies a model’s capacity to capture temporal dependencies.
Pattern Learning Score evaluates the representation quality in extracting discriminative temporal patterns.
Task Adaptation Score assesses cross-task generalization capability, enabling versatile time series analysis.

TEMPLATE presents a versatile framework compatible with both classification and regression paradigms. Through comprehensive benchmarking across five distinct downstream tasks, our method demonstrates superior capability in identifying optimal pre-trained models from heterogeneous model pools for transfer learning. Compared to the state-of-the-art method ETran, our approach improves the weighted Kendall's

τ_{w}

across five downstream tasks by 35\%.

The code is available at https://anonymous.4open.science/r/TEMPLATE-A0AA/.