Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

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 …

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

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 …

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 …

Time series analysis is essential for comprehending the complexities inherent in various real-
world systems and applications. Although large language models (LLMs) have recently …

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 …

Deep Learning has been successfully applied to many application domains, yet its
advantages have been slow to emerge for time series forecasting. For example, in the well …

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

As a core branch of financial forecasting, stock forecasting plays a crucial role for financial
analysts, investors, and policymakers in managing risks and optimizing investment …