IEEE

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Abstract: 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 ...
IEEE

Fusion Graph Structure Learning-Based Multivariate Time Series Anomaly Detection With Structured Prior Knowledge

Abstract: Multivariate time series anomaly detection (MTSAD) plays a crucial role in the Internet of Things (IoT) to identify device malfunction or system attacks. Graph neural networks (GNN) are ...
GitHub

Unified Training of Universal Time Series Forecasting Transformers

Uni2TS is a PyTorch based library for research and applications related to Time Series Transformers. This library aims to provide a unified solution to large-scale pre-training of Universal Time ...
GitHub

Toto - Time Series Optimized Transformer for Observability

Toto is a foundation model for multivariate time series forecasting with a focus on observability metrics. This model leverages innovative architectural designs to efficiently handle the ...