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：本文提出了一种关于时间序列预测的新视角。在现有的时间序列预测方法中，模型以数值序列作为输入并输出数值结果。当前最先进的模型主要基于Transformer架构，通过多种编码机制对历史数据的上下文和语义进行整合。受预训练语言基础模型成功应用的启发，我们提出一个问题：这些模型是否也能被改造以解决时间序列预测问题？因此，我们提出了一种新的预测范式：基于提示的时间序列预测（PromptCast）。在这一新颖任务中，数值输入和输出被转化为提示，预测任务以句子到句子的方式构建，从而使得语言模型能够直接用于预测目的。为支持和促进该任务的研究，我们还提供了一个大规模数据集（PISA），其中包含三个真实世界的预测场景。我们评估了不同的最先进数值预测方法和语言生成模型。多种预测设置下的基准测试结果表明，所提出的基于语言生成模型的PromptCast是一个有前景的研究方向。此外，与传统基于数值的预测相比，PromptCast在零样本设置下展现出更强的泛化能力。