Large language models (LLMs) have received considerable attention recently due to their
outstanding comprehension and reasoning capabilities, leading to great progress in many …

This systematic literature review comprehensively examines the application of Large
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Motivated by recent advances in large language models for Natural Language Processing
(NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero …

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 …

We introduce MOMENT, a family of open-source foundation models for general-purpose
time series analysis. Pre-training large models on time series data is challenging due to (1) …

Deep learning for time series forecasting has traditionally operated within a one-model-per-
dataset framework, limiting its potential to leverage the game-changing impact of large pre …

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 …

The vast majority of time-series forecasting approaches require a substantial training
dataset. However, many real-life forecasting applications have very little initial observations …

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 …