The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. On the other hand, for natural language processing, Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. In this paper, we propose a novel framework, TEMPO, that can effectively learn time series representations. We focus on utilizing two essential inductive biases of the time series task for pre-trained models: (i) decomposition of the complex interaction between trend, seasonal and residual components; and (ii) introducing the selection-based prompts to facilitate distribution adaptation in non-stationary time series. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains. Our experiments demonstrate the superior performance of TEMPO, with 20\%-60\% improvement over state-of-the-art methods on a number of time series benchmark datasets. This performance gain is observed not only in standard supervised learning settings but also in scenarios involving previously unseen datasets. This compelling finding highlights \modelname's potential to constitute a foundational model building framework.

TEMPO是一个基于时间序列的新框架，通过利用时序任务的两个基本归纳偏好来有效学习时间序列表示，展现出超过20%至60%的改善性能，并在标准监督学习和之前未见数据集的情景中观察到。

TEMPO：面向时间序列预测的基于提示的生成预训练变换器