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

Collections of time series formed via aggregation are prevalent in many fields. These are
commonly referred to as hierarchical time series and may be constructed cross-sectionally …

By encoding time series as a string of numerical digits, we can frame time series forecasting
as next-token prediction in text. Developing this approach, we find that large language …

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

We study the problem of uncertainty quantification for time series prediction, with the goal of
providing easy-to-use algorithms with formal guarantees. The algorithms we present build …

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 research focuses on the profound impact of the shocks caused by the COVID-19
pandemic on the accuracy of AI-based demand forecasting in the beauty care industry. It …

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 …

To quantify uncertainty, conformal prediction methods are gaining continuously more
interest and have already been successfully applied to various domains. However, they are …

COVID-19 has caused negative impacts on blood supply chain management, due to
uncertain supply/demand and logistical disruptions. In the early weeks following the COVID …