筆者對KDD 2021會議的錄用論文,與時空資料探勘相關的論文進行了梳理,共有19篇,按照研究任務對論文進行分類整理,如下:
【時間序列分析-3篇】
1。 ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting
2。 Statistical models coupling allows for complex local multivariate time series analysis
3。 Causal and Interpretable Rules for Time Series Analysis
【交通預測-3篇】
1。 TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction
2。 Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
3。 Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
【異常檢測-3篇】
1。 Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding
2。 Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization
3。 Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering
【時空資料建模-1篇】
1。 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
【時空預測不確定性量化-1篇】
1。 Quantifying Uncertainty in Deep Spatiotemporal Forecasting
【時間序列表示學習-1篇】
1。 Representation Learning of Multivariate Time Series using a Transformer Framework
【移動預測-1篇】
1。 A PLAN for Tackling the Locust Crisis in East Africa: Harnessing Spatiotemporal Deep Models for Locust Movement Forecasting
【氣象預測-1篇】
1。 Micro-climate Prediction - Multi scale encoder-decoder based deep learning framework
【POI推薦-1篇】
1。 Curriculum Meta-Learning for Next POI Recommendation
【POI補全-1篇】
1。 Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps
【行程時間估計-1篇】
1。 SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps
【軌跡相似性計算-1篇】
1。 A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks
【軌跡修復-1篇】
1。 MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning
參考資料:
KDD 2021錄取論文完整列表:
2。 時空資料探勘論文-IJCAI 2021:
3。 時空資料探勘論文-AAAI 2021:
4。 近三年用於時空資料探勘的GNN論文彙總: