Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

Large language models (LLMs) are being applied to time series forecasting. But are
language models actually useful for time series? In a series of ablation studies on three …

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …

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 …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

This paper introduces a novel approach that leverages Large Language Models (LLMs) and
Generative Agents to enhance time series forecasting by reasoning across both text and …

Large Language Models (LLMs) have seen significant use in domains such as natural
language processing and computer vision. Going beyond text, image and graphics, LLMs …

In an era marked by the increasing adoption of Large Language Models (LLMs) for various
tasks, there is a growing focus on exploring LLMs' capabilities in handling web data …

Large language models (LLMs) have been applied in many fields and have developed
rapidly in recent years. As a classic machine learning task, time series forecasting has …

Traffic prediction, a critical component for intelligent transportation systems, endeavors to
foresee future traffic at specific locations using historical data. Although existing traffic …