This paper presents a new perspective on time series forecasting. In existing time series forecasting methods, the models take a sequence of numerical values as input and yield numerical values as output. The existing SOTA models are largely based on the Transformer architecture, modified with multiple encoding mechanisms to incorporate the context and semantics around the historical data. Inspired by the successes of pre-trained language foundation models, we pose a question about whether these models can also be adapted to solve time-series forecasting. Thus, we propose a new forecasting paradigm: prompt-based time series forecasting (PromptCast). In this novel task, the numerical input and output are transformed into prompts and the forecasting task is framed in a sentence-to-sentence manner, making it possible to directly apply language models for forecasting purposes. To support and facilitate the research of this task, we also present a large-scale dataset (PISA) that includes three real-world forecasting scenarios. We evaluate different SOTA numerical-based forecasting methods and language generation models. The benchmark results with various forecasting settings demonstrate the proposed PromptCast with language generation models is a promising research direction. Additionally, in comparison to conventional numerical-based forecasting, PromptCast shows a much better generalization ability under the zero-shot setting.

翻译：本文提出了一种时间序列预测的新视角。现有时间序列预测方法以数值序列作为输入并输出数值结果。当前最先进的模型大多基于Transformer架构，通过多种编码机制融入历史数据的上下文和语义信息。受预训练语言基础模型成功经验的启发，我们探究了这些模型能否适配解决时间序列预测问题。为此，我们提出了一种新的预测范式：基于提示学习的时间序列预测（PromptCast）。在此新任务中，数值输入与输出被转换为提示形式，预测任务以句子到句子的方式进行，从而可直接将语言模型用于预测。为支持并推动该任务研究，我们还构建了一个包含三种真实预测场景的大规模数据集（PISA）。我们评估了不同最先进的数值预测方法与语言生成模型。多场景下的基准测试结果表明，基于语言生成模型的PromptCast是一个极具前景的研究方向。此外，与传统数值预测方法相比，PromptCast在零样本设置下展现出更优的泛化能力。