首次评估时间序列大模型的持续学习能力，发现其能越学越好。
Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning
- 设计新框架测试模型在数据变化下的持续学习表现
- 大模型在增量微调中准确率持续提升，传统模型则下降
- 适合关注长期可迭代模型研发的研究者
时间序列大模型在多种预测任务中表现优异，但其通过增量学习实现持续改进的能力尚未被探索。本文首次系统研究这些模型的时序可塑性——即在保持已有能力的同时，通过持续学习逐步提升性能。我们在存在分布偏移的真实数据集上，采用新型持续学习框架评估了传统深度学习模型与大模型。结果表明，尽管传统模型在增量微调中性能明显下降，但像Time-MoE和Chronos这样的大模型却展现出预测准确率的持续提升。这说明优化大模型的微调策略可能比开发领域专用的小模型更具价值。本研究提出了新的评估方法与洞见，为构建具备强持续学习能力的时间序列大模型提供支持。
原文摘要 · Abstract (English)
Time series foundation models excel at diverse time series forecasting tasks, but their capacity for continuous improvement through incremental learning remains unexplored. We present the first comprehensive study investigating these models' temporal plasticity - their ability to progressively enhance performance through continual learning while maintaining existing capabilities. Through experiments on real-world datasets exhibiting distribution shifts, we evaluate both conventional deep learning models and foundation models using a novel continual learning framework. Our findings reveal that while traditional models struggle with performance deterioration during incremental fine-tuning, foundation models like Time-MoE and Chronos demonstrate sustained improvement in predictive accuracy. This suggests that optimizing foundation model fine-tuning strategies may be more valuable than developing domain-specific small models. Our research introduces new evaluation methodologies and insights for developing foundation time series models with robust continuous learning capabilities.
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