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 …

Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …

The ubiquitous missing values cause the multivariate time series data to be partially
observed, destroying the integrity of time series and hindering the effective time series data …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

The study of time series data is crucial for understanding trends and anomalies over time,
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …

Monitoring of energy conversion process confronts great difficulties due to extreme value
jumps or data packet loss under extreme operating conditions, consequently resulting in …

Time series forecasting (TSF) plays a pivotal role in many real-world applications. Recently,
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …

Imputation of Correlated Time Series (CTS) is essential in data preprocessing for many
tasks, particularly when sensor data is often incomplete. Deep learning has enabled …

Traffic forecasting is a challenging research topic due to the complex spatial and temporal
dependencies among different roads. Though great efforts have been made on traffic …