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 …

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been
successfully applied in many fields. The gradual application of the latest architectures of …

The recent boom of linear forecasting models questions the ongoing passion for
architectural modifications of Transformer-based forecasters. These forecasters leverage …

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 …

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 …

Multivariate time series forecasting poses an ongoing challenge across various disciplines.
Time series data often exhibit diverse intra-series and inter-series correlations, contributing …

Foundation models of time series have not been fully developed due to the limited
availability of time series corpora and the underexploration of scalable pre-training. Based …

RNN-based methods have faced challenges in the Long-term Time Series Forecasting
(LTSF) domain when dealing with excessively long look-back windows and forecast …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are first-principled, explainable, and sample-efficient …

Deep learning has contributed remarkably to the advancement of time series analysis. Still,
deep models can encounter performance bottlenecks in real-world data-scarce scenarios …