【ICLR 2022】时空数据挖掘论文

对ICLR 2022的录用论文进行了整理，筛选出其中与时空数据挖掘相关论文，并进行任务分类。

ICLR 2022完整录用论文列表：https://openreview.net/group?id=ICLR.cc/2022/Conference#oral-submissions

Time series signal analysis

1. T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis. Minhao LIU, Ailing Zeng, Qiuxia LAI, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu

Time series forecasting

1. Multivariate Time Series Forecasting with Latent Graph Inference. Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
2. TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting. Yuzhou Chen, Ignacio Segovia-Dominguez, Baris Coskunuzer, Yulia Gel
3. Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, Schahram Dustdar
4. DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting. Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu
5. CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting. Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
6. Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift. Taesung Kim, Jinhee Kim, Yunwon Tae, Cheonbok Park, Jang-Ho Choi, Jaegul Choo

Time series classification

1. Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification. Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang

Time series anomaly detection

1. Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. Enyan Dai, Jie Chen
2. Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. Jiehui Xu, Haixu Wu, Jianmin Wang, Mingsheng Long

Time series imputation

1. Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks. Andrea Cini, Ivan Marisca, Cesare Alippi

Spatial-Temporal Representation Learning

1. UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning. Kunchang Li, Yali Wang, Gao Peng, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao
2. Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs. Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan

Spatial-temporal GNN

1. Space-Time Graph Neural Networks. Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro

Traffic forecasting

1. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. Hyunwook Lee, Seungmin Jin, Hyeshin Chu, Hongkyu Lim, Sungahn Ko

Trajectory prediction

1. You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf