Time-PEFT: Temporal and Multichannel Complexity-Based Fine-Tuning for Time-Series Foundation Models

Recent studies have attempted to fine-tune time-series foundation models to enhance a target dataset's forecasting performance. However, these approaches proceed without a clear criterion for identifying complex datasets that require fine-tuning due to performance degradation in zero-shot forecasting. To distinguish datasets that are more challenging than standard benchmarks, we introduce data-driven temporal complexity and multichannel complexity. Temporal complexity captures the difficulty of identifying distinct patterns by quantifying spectral entropy in the frequency domain, while multichannel complexity captures cross-channel information flow that can impact predictive uncertainty. These metrics serve as effective proxies for performance gains achievable through fine-tuning. Based on the two metrics, we develop Time-PEFT, a parameter-efficient fine-tuning framework that incorporates a frequency adapter for top-$k$ filtering and a channel adapter for multichannel modeling. With the base variant of MOMENT as a backbone, Time-PEFT improves performance by up to 38% over LoRA on complex datasets.