Time series forecasting is a critical task that provides key information for decision-making
across various fields. Recently, various fundamental deep learning architectures such as …

Large Language Models (LLMs) have transformed Natural Language Processing (NLP) and
Software Engineering by fostering innovation, streamlining processes, and enabling data …

Advancements in deep learning tools originally designed for natural language processing
are also applied to applications in the field of process control. Transformers, in particular …

Time series foundation models excel in zero-shot forecasting, handling diverse tasks without
explicit training. However, the advancement of these models has been hindered by the lack …

Pre-trained models have become pivotal in enhancing the efficiency and accuracy of time
series forecasting on target data sets by leveraging transfer learning. While benchmarks …

Multivariate Time Series Forecasting (MTSF) plays a crucial role across diverse fields,
ranging from economic, energy, to traffic. In recent years, deep learning has demonstrated …

Early prediction of pediatric cardiac arrest (CA) is critical for timely intervention in high-risk
intensive care settings. We introduce PedCA-FT, a novel transformer-based framework that …

Universal knowledge representation is a central problem for multivariate time series (MTS)
foundation models and yet remains open. This paper investigates this problem from the first …

Numerous industrial sectors necessitate models capable of providing robust forecasts
across various horizons. Despite the recent strides in crafting specific architectures for time …

We introduce the concept of Hyper-Trees and offer a new direction in applying tree-based
models to time series data. Unlike conventional applications of decision trees that forecast …