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Found 5 papers, 5 papers with code
Universal Time-Series Representation Learning: A Survey
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies.
Context-Aware Deep Time-Series Decomposition for Anomaly Detection in Businesses
Detecting anomalies in time series has become increasingly challenging as data collection technology develops, especially in realworld communication services, which require contextual information for precise prediction.
AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series
Here, we show that AnoViz streamlines the process of finding a potential cause of an anomaly with a deeper analysis of anomalous instances, giving explainability to any anomaly detector.
MG-TAR: Multi-View Graph Convolutional Networks for Traffic Accident Risk Prediction
Due to the continuing colossal socio-economic losses caused by traffic accidents, it is of prime importance to precisely forecast the traffic accident risk to reduce future accidents.
DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior
Because traffic accidents cause huge social and economic losses, it is of prime importance to precisely predict the traffic accident risk for reducing future accidents.
