Foundation models for time series analysis: A tutorial and survey
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 …
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …
Crossgnn: Confronting noisy multivariate time series via cross interaction refinement
Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …
development and widespread applications across various fields. Transformer-based and …
Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting
Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …
Recently, some methods have employed patching to enhance local semantics and improve …
Deep learning for multivariate time series imputation: A survey
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 …
observed, destroying the integrity of time series and hindering the effective time series data …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
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 …
data is continuously collected through streaming manners, which has propelled the …
A survey on diffusion models for time series and spatio-temporal data
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 …
enabling predictive insights across various sectors. Spatio-temporal data, on the other hand …
Denoising Diffusion Straightforward Models for Energy Conversion Monitoring Data Imputation
Monitoring of energy conversion process confronts great difficulties due to extreme value
jumps or data packet loss under extreme operating conditions, consequently resulting in …
jumps or data packet loss under extreme operating conditions, consequently resulting in …
[PDF][PDF] LeRet: Language-Empowered Retentive Network for Time Series Forecasting
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 …
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …
ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions
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 …
tasks, particularly when sensor data is often incomplete. Deep learning has enabled …
Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic Forecasting
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 …
dependencies among different roads. Though great efforts have been made on traffic …