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Found 3 papers, 3 papers with code
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.
Ranked #1 on
Traffic Prediction
on PeMSD7
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
