PromptCast: A New Prompt-Based Learning Paradigm for Time Series Forecasting

By Hao Xue, Flora D.Salim. IEEE Transactions on Knowledge and Data Engineering, 2022

362 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/863171ed35ca0035074f73bb202b153cc346f2f3

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Citations in the corpus

Large Language Models Are Zero-Shot Time Series Forecasters (2023, methodology): “The only method we are aware of is PromptCast [50], which poses forecasting as question answering with prompting.”
Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities (2024, methodology): “When designing hard prompts for time-series prediction with language models, the general guideline is to transform numerical data into a format that mimics natural language constructs [187].”
Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook (2024, neutral): “Significant advancements have been made in M-LLMs with temporal analysis capabilities, including models such as Time-LLM [90], LLM4TS [91], TEMPO [92], and PromptCast [93], among others [94,95].”