The diversity and domain dependence of time series data pose significant challenges in transferring learning to time series forecasting. In this study, we examine the effectiveness of using a transformer model that has been pre-trained on natural language or image data and then fine-tuned for time series forecasting with minimal modifications, specifically, without altering the self-attention and feedforward layers of the residual blocks. This model, known as the Frozen Pretrained Transformer (FPT), is evaluated through fine-tuning on time series forecasting tasks under Zero-Shot, Few-Shot, and normal sample size conditions. Our results demonstrate that pre-training on natural language or images can lead to a comparable or state-of-the-art performance in cross-modality time series forecasting tasks, in contrast to previous studies that focused on fine-tuning within the same modality as the pre-training data. Additionally, we provide a comprehensive theoretical analysis of the universality and the functionality of the FPT. The code is publicly available at https://anonymous.4open.science/r/Pretrained-LM-for-TSForcasting-C561.

翻译：时间序列数据的多样性和领域依赖性给迁移学习在时间序列预测中的应用带来了重大挑战。本研究探究了使用预训练于自然语言或图像数据、并通过最小化改造（即不修改残差块的自注意力层和前馈层）进行微调后用于时间序列预测的Transformer模型的有效性。该模型被称为冻结预训练Transformer（Frozen Pretrained Transformer, FPT），在零样本、少样本及常规样本量条件下通过时间序列预测任务的微调进行评估。我们的结果表明，与以往聚焦于微调数据需与预训练数据同一模态的研究不同，预训练于自然语言或图像数据可在跨模态时间序列预测任务中取得相当或最优性能。此外，我们对FPT的普适性和功能性进行了全面的理论分析。代码已公开于https://anonymous.4open.science/r/Pretrained-LM-for-TSForcasting-C561。