Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
ST traffic forecasting models need to address out-of-distribution issues caused by spatio-temporal shifts and external factors, such as time and weather variations.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Traffic forecasting is enhanced by COOL, a model that captures high-order spatio-temporal relationships for accurate predictions.
LSTTN: Long-Short Term Transformer-based Spatio-temporal Neural Network for Traffic Flow Forecasting
Proposing a novel LSTTN framework for accurate traffic flow forecasting by integrating long- and short-term features.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Proposing the COOL model to capture high-order spatio-temporal relationships in traffic forecasting.
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
The authors propose a theoretical solution named Disentangled Contextual Adjustment (DCA) to address the out-of-distribution (OOD) issue in traffic data forecasting. They introduce the Spatio-Temporal sElf-superVised dEconfounding (STEVE) framework to improve generalization ability.
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
The author proposes TESTAM, a Mixture-of-Experts model, to enhance in-situ traffic forecasting by incorporating diverse graph architectures. By transforming the routing problem into a classification task, TESTAM effectively contextualizes various traffic conditions and selects appropriate spatial modeling methods.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
The author proposes the COOL model to capture high-order spatio-temporal relationships in traffic forecasting by utilizing heterogeneous graphs and self-attention decoders.
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting Analysis
The author proposes a Wavelet-Inspired Graph Convolutional Recurrent Network (WavGCRN) to combine multiscale analysis with deep learning for accurate traffic forecasting.