Time-Series Mining (TSM) is an important research area since it shows great potential in
practical applications. Deep learning models that rely on massive labeled data have been …

Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …

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 …

In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern
and distill hidden patterns within historical time series data to forecast future states …

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

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

Deep models have demonstrated remarkable performance in time series forecasting.
However, due to the partially-observed nature of real-world applications, solely focusing on …

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 …

Recent efforts have been dedicated to enhancing time series forecasting accuracy by
introducing advanced network architectures and self-supervised pretraining strategies …

Given the ubiquitous presence of time series data across various domains, precise
forecasting of time series holds significant importance and finds widespread real-world …