ChronoTab: Forecasting Multivariate Time Series with Tabular LLMs

Forecasting future values in multivariate time series is a critical challenge in many application domains, such as agriculture, transportation, energy, etc. Recently, Large Language Models (LLMs) have been used for time series analysis tasks. However, they are typically limited to handling univariate time series, since their input has the form of a single sequence. The problem of understanding and processing multiple dimensions also arises when using LLMs to analyze tabular data, where each table has multiple columns. To improve the capability of LLMs to handle tabular data, some recent works have relied on fine-tuning an LLM to perform various table-related tasks, such as missing values imputation, on large-scale table corpora. In this paper, our goal is to investigate whether such an LLM that has been fine-tuned on tabular data can exhibit better performance when used for multivariete time series forecasting. In particular, we present ChronoTab, an approach that utilizes the tabular format of multivariate time series to create prompts with a specific context and then via model inference enables zero-shot multivariate forecasting. Our experiments show that ChronoTab improves forecasting accuracy, outperforming in most cases both pre-trained LLMs and state-of-the-art methods.