【NeurIPS 2023】时空数据挖掘论文

对NeurIPS 2023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，如下。

NeurIPS 2023论文录用列表： https://openreview.net/group?id=NeurIPS.cc/2023/Conference

Automatic Integration for Spatiotemporal Neural Point Processes. Zihao Zhou, Rose Yu. paper
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective. Zhiding Liu, Mingyue Cheng, Zhi Li, Zhenya Huang, Qi Liu, Yanhu Xie, Enhong Chen. paper
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis. Zelin Ni, Hang Yu, Shizhan Liu, Jianguo Li , Weiyao Lin. paper
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series. Andrea Nascetti, Ritu Yadav, Kirill Brodt, Qixun Qu, Hongwei Fan, Yuri Shendryk, Isha Shah, Christine Chung. paper
Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis. Yuanshao Zhu, Yongchao Ye, Ying Wu, Xiangyu Zhao, James Yu. paper
Contrast Everything: Multi-Granularity Representation Learning for Medical Time-Series. Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang. paper
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling. Yuqi Chen, Kan Ren, Yansen Wang, Yuchen Fang, Weiwei Sun, Dongsheng Li. paper
Causal Discovery from Subsampled Time Series with Proxy Variables. Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang. paper
Causal Discovery in Semi-Stationary Time Series. Shanyun Gao, Raghavendra Addanki, Tong Yu, Ryan Rossi, Murat Kocaoglu. paper
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement. Qihe Huang, Lei Shen, Ruixin Zhang, Shouhong Ding, Binwu Wang, Zhengyang Zhou, Yang Wang. paper
Conformal Prediction for Time Series with Modern Hopfield Networks. Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter. paper
Conformal Scorecasting: Anticipatory Uncertainty Quantification for Distribution Shift in Time Series. Anastasios Angelopoulos, Ryan Tibshirani, Emmanuel Candes. paper
DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model. Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James Yu. paper
DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting. Salva Rühling Cachay, Bo Zhao, Hailey James, Rose Yu. paper
Drift doesn’t Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection. Chengsen Wang, Qi Qi, Jingyu Wang, Haifeng Sun, Xingyu Wang, Zirui Zhuang, Jianxin Liao. paper code
Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment. Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann. paper
Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics. Liming Wu, Zhichao Hou, Jirui Yuan, Yu Rong, Wenbing Huang. paper
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency. Owen Queen, Thomas Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik. paper
Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics. Koen Minartz, Yoeri Poels, Simon Koop, Vlado Menkovski. paper
Fine-Grained Spatio-Temporal Particulate Matter Dataset From Delhi For ML based Modeling. Sachin Chauhan, Zeel Bharatkumar Patel, Sayan Ranu, Rijurekha Sen, Nipun Batra. paper
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting. Kun Yi, Qi Zhang, Wei Fan, Hui He, Pengyang Wang, Shoujin Wang, Ning An, Defu Lian, Longbing Cao, Zhendong Niu. paper
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space. Shengzhong Liu, Tomoyoshi Kimura, Dongxin Liu, Ruijie Wang, Jinyang Li, Suhas Diggavi, Mani Srivastava, Tarek Abdelzaher. paper
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective. Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, Zhendong Niu. paper
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning. Berken Utku Demirel, Christian Holz. paper code
Generative Pre-Training of Spatio-Temporal Graph Neural Networks. Zhonghang Li, Lianghao Xia, Yong Xu, Chao Huang. paper
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. Yixuan Zhang, Quyu Kong, Feng Zhou. paper
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors. Yong Liu, Chenyu Li, Jianmin Wang, Mingsheng Long. paper code
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting. Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, Roger Zimmermann. paper code
Large Language Models Are Zero Shot Time Series Forecasters. Marc Finzi, Nate Gruver, Shikai Qiu, Andrew Wilson. paper
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection. Junho Song, Keonwoo Kim, Jeonglyul Oh, Sungzoon Cho. paper code
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction. Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao, Jeffrey H Lang, Duane Boning. paper code
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning. Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, Stan Z. Li. paper code
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling. Yifan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan. paper code
One Fits All: Power General Time Series Analysis by Pretrained LM. Tian Zhou, Peisong Niu, xue wang, Liang Sun, Rong Jin. paper
On the Constrained Time-Series Generation Problem. Andrea Coletta, Sriram Gopalakrishnan, Daniel Borrajo, Svitlana Vyetrenko. paper
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting. Marcel Kollovieh, Abdul Fatir Ansari, Michael Bohlke-Schneider, Jasper Zschiegner, Hao Wang, Yuyang (Bernie) Wang. paper
SpatialRank: Urban Event Ranking with NDCG Optimization on Spatiotemporal Data. Bang An, Xun Zhou, Yongjian Zhong, Tianbao Yang. paper
Sparse Graph Learning from Spatiotemporal Time Series. Andrea Cini, Daniele Zambon, Cesare Alippi. paper
Sparse Deep Learning for Time Series Data: Theory and Applications. Mingxuan Zhang, Yan Sun, Faming Liang. paper
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels. Zhen Liu, ma peitian, Dongliang Chen, Wenbin Pei, Qianli Ma. paper code
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling. Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang, Mingsheng Long. paper code
Taming Local Effects in Graph-based Spatiotemporal Forecasting. Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi. paper
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings. Giovanni De Felice, John Goulermas, Vladimir Gusev. paper
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series. Zekun Li, Shiyang Li, Xifeng Yan. paper
UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction. Yansong Ning, Hao Liu, Hao Wang, Zhenyu Zeng, Hui Xiong. paper code
What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context? Oussama Boussif, Ghait Boukachab, Dan Assouline, Stefano Massaroli, Tianle Yuan, Loubna Benabbou, Yoshua Bengio. paper code
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting. Yuxin Jia, Youfang Lin, Xinyan Hao, Yan Lin, Shengnan Guo, Huaiyu Wan. paper code
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction. Sebastian Gerard, Yu Zhao, Josephine Sullivan. paper