NeurIPS 2024于2024年12月10号-12月15号在加拿大温哥华举行（Vancouver, Canada）,录取率25.8%
本文总结了NeurIPS 2024有关时间序列（time series data）的相关论文，主要包含如有疏漏，欢迎大家补充。
时间序列Topic：预测，插补，分类，生成，因果分析，异常检测，LLM以及基础模型等内容。总计61篇，其中正会55篇，D&B Track6篇
预测：1-29
异常检测：30，57
分类：32，54，55
表示学习：37，39，40
生成：31，41，42，60
时序分析：33，34，36
大语言模型：7，10，24，52
基础模型：16，29，35，53
扩散模型：1，31，42，43
Retrieval-Augumented Diffusion Models for Time Series Forecasting Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting FilterNet: Harnessing Frequency Filters for Time Series Forecasting Frequency Adaptive Normalization For Non-stationary Time Series Forecasting Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective AutoTimes: Autoregressive Time Series Forecasters via Large Language Models DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting BackTime: Backdoor Attacks on Multivariate Time Series Forecasting Are Language Models Actually Useful for Time Series Forecasting? Rethinking Fourier Transform for Long-term Time Series Forecasting: A Basis Functions Perspective Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion Multivariate Probabilistic Time Series Forecasting with Correlated Errors CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting Scaling Law for Time Series Forecasting From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection From Similarity to Superiority: Channel Clustering for Time Series Forecasting TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer Are Self-Attentions Effective for Time Series Forecasting? Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis Shape analysis for time series UNITS: A Unified Multi-Task Time Series Model Large Pre-trained time series models for cross-domain Time series analysis tasks "Segment, Shuffle, and Stitch: A Simple Mechanism for Improving Time-Series Representations" Task-oriented Time Series Imputation Evaluation via Generalized Representers Exploiting Representation Curvature for Boundary Detection in Time Series Learning diverse causally emergent representations from time series data SDformer: Similarity-driven Discrete Transformer For Time Series Generation FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation ANT: Adaptive Noise Schedule for Time Series Diffusion Models Trajectory Flow Matching with Applications to Clinical Time Series Modelling Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models Reinforced Cross-Domain Knowledge Distillation on Time Series Data Boosting Transferability and Discriminability for Time Series Domain Adaptation Towards Editing Time Series Conformalized Time Series with Semantic Features ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization UniMTS: Unified Pre-training for Motion Time Series Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification D&B Track
IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark Building Timeseries Dataset: Empowering Large-Scale Building Analytics Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
1 Retrieval-Augumented Diffusion Models for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/94339
作者：Jingwei Liu, Ling Yang, Hongyan Li, Shenda Hong
关键词：预测，扩散模型，检索增强
2 Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
链接：https://neurips.cc/virtual/2024/poster/94220
arXiv：https://arxiv.org/abs/2402.11463
作者：Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang
关键词：长时预测
3 Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95175
作者：Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui
关键词：预测，多尺度，超图，Transformer
4 FilterNet: Harnessing Frequency Filters for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/93257
作者：Kun Yi, Wei Fan, Qi Zhang, Hui He, Jingru Fei, Shufeng Hao, Defu Lian
关键词：预测，频率过滤
5 Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95063
arXiv：https://arxiv.org/abs/2409.20371
作者：Weiwei Ye · Songgaojun Deng · Qiaosha Zou · Ning Gui
关键词：预测，非平稳
6 Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
链接：https://neurips.cc/virtual/2024/poster/96026
arXiv：https://arxiv.org/abs/2409.18696
作者：Chengsen Wang · Qi Qi · Jingyu Wang · Haifeng Sun · Zirui Zhuang · Jinming Wu · Jianxin Liao
关键词：预测，稳健性
7 AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
链接：https://neurips.cc/virtual/2024/poster/95975
arXiv：https://arxiv.org/abs/2402.02370
作者：Yong Liu · Guo Qin · Xiangdong Huang · Jianmin Wang · Mingsheng Long
关键词：预测，LLM，自回归
AI论文速读 | AutoTimes：利用大语言模型的自回归时间序列预测器
8 DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95167
作者：Tao Dai · Beiliang Wu · Peiyuan Liu · Naiqi Li · Xue Yuerong · Shu-Tao Xia · Zexuan Zhu
关键词：预测，非平稳，双域
9 BackTime: Backdoor Attacks on Multivariate Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95645
arXiv：https://arxiv.org/abs/2410.02195
作者：Xiao Lin · Zhining Liu · Dongqi Fu · Ruizhong Qiu · Hanghang Tong
关键词：预测，后门攻击
10 [Spotlight] Are Language Models Actually Useful for Time Series Forecasting?
链接：https://neurips.cc/virtual/2024/poster/96085
arXiv：https://arxiv.org/abs/2410.02195
作者：Mingtian Tan · Mike Merrill · Vinayak Gupta · Tim Althoff · Tom Hartvigsen
关键词：预测，LLM
备注：大胆之作，去掉LLM效果更好了。
圆圆的算法笔记：预训练大语言模型对时间序列预测真的有用吗？去掉预训练LLM效果反而提升
11 Rethinking Fourier Transform for Long-term Time Series Forecasting: A Basis Functions Perspective
链接：https://neurips.cc/virtual/2024/poster/96209
作者：Runze Yang · Longbing Cao · JIE YANG · li jianxun
关键词：长时预测，傅里叶变换
12 Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/94305
作者：Bong Gyun Kang · Dongjun Lee · HyunGi Kim · Dohyun Chung · Sungroh Yoon
关键词：预测，谱域注意力，长期依赖
13 Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/93133
arXiv：https://arxiv.org/abs/2401.11929
作者：Jinliang Deng · Feiyang Ye · Du Yin · Xuan Song · Ivor Tsang · Hui Xiong
关键词：长时预测
14 Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics
链接：https://neurips.cc/virtual/2024/poster/94383
作者：Xiaodan Chen · Xiucheng Li · Xinyang Chen · Zhijun Li
关键词：预测，可解释性，动态性
15 DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching
链接：https://neurips.cc/virtual/2024/poster/96221
作者：Donghao Luo · Xue Wang
关键词：预测，Transformer，Patch
16 Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95835
arXiv：https://arxiv.org/abs/2405.14252
作者：Qingxiang Liu · Xu Liu · Chenghao Liu · Qingsong Wen · Yuxuan Liang
关键词：预测，联邦学习，基础模型
17 PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/92992
arXiv：https://arxiv.org/abs/2409.17703
代码：https://github.com/Water2sea/TPGN
作者：Yuxin Jia · Youfang Lin · Jing Yu · Shuo Wang · Tianhao Liu · Huaiyu Wan
关键词：长时预测，RNN
18 SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
链接：https://neurips.cc/virtual/2024/poster/96390
arXiv：https://arxiv.org/abs/2404.14197
代码：https://github.com/Secilia-Cxy/SOFTS
作者：Han Lu · Xu-Yang Chen · Han-Jia Ye · De-Chuan Zhan
关键词：预测，MLP
19 Multivariate Probabilistic Time Series Forecasting with Correlated Errors
链接：https://neurips.cc/virtual/2024/poster/94440
arXiv：https://arxiv.org/abs/2409.18479
作者：Zhihao Zheng · Lijun Sun
关键词：概率预测，不确定性量化
20 CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
链接：https://neurips.cc/virtual/2024/poster/94391
arXiv：https://arxiv.org/abs/2409.18479
代码：https://github.com/ACAT-SCUT/CycleNet
作者：Shengsheng Lin · Weiwei Lin · Xinyi Hu · Wentai Wu · Ruichao Mo · Haocheng Zhong
关键词：长时预测，周期建模
科学最Top：时序论文28｜CycleNet：通过对周期模式进行建模增强时间序列预测
21 Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95988
作者：Romain Ilbert · Malik Tiomoko · Cosme Louart · Ambroise Odonnat · Vasilii Feofanov · Themis Palpanas · Ievgen Redko
关键词：预测，多任务回归，随机矩阵理论
22 CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95627
arXiv：https://arxiv.org/abs/2406.02131
代码：https://github.com/RafaDD/CondTSF
作者：Jianrong Ding · Zhanyu Liu · Guanjie Zheng · Haiming Jin · Linghe Kong
关键词：预测，插件
23 Scaling Law for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/96119
arXiv：https://arxiv.org/pdf/2405.15124
代码：https://github.com/JingzheShi/ScalingLawForTimeSeriesForecasting
作者：Jingzhe Shi · Qinwei Ma · Huan Ma · Lei Li
关键词：预测，Scaling Law
24 From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
链接：https://neurips.cc/virtual/2024/poster/93316
arXiv：https://arxiv.org/abs/2409.17515
作者：Xinlei Wang · Maike Feng · Jing Qiu · Jinjin Gu · Junhua Zhao
关键词：预测，LLM，事件融合
25 From Similarity to Superiority: Channel Clustering for Time Series Forecasting
链接：https://neurips.cc/virtual/2024/poster/95539
arXiv：https://arxiv.org/abs/2404.01340
作者：Jialin Chen · Jan Eric Lenssen · Aosong Feng · Weihua Hu · Matthias Fey · Leandros Tassiulas · Jure Leskovec · Rex Ying
关键词：预测，通道聚类
AI论文速读 | CCM：从相似到超越：时间序列预测的通道聚类
26 TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
链接：https://neurips.cc/virtual/2024/poster/95770
arXiv：https://arxiv.org/abs/2402.19072
作者：Yuxuan Wang · Haixu Wu · Jiaxiang Dong · Guo Qin · Haoran Zhang · Yong Liu · Yun-Zhong Qiu · Jianmin Wang · Mingsheng Long
关键词：预测，外生变量，Transformer
AI论文速读 | TimeXer：让 Transformer能够利用外部变量进行时间序列预测
27 ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
链接：https://neurips.cc/virtual/2024/poster/93264
作者：Jiawen Zhang · Shun Zheng · Xumeng Wen · Xiaofang Zhou · Jiang Bian · Jia Li
关键词：预测，稳健性，Patch
28 Are Self-Attentions Effective for Time Series Forecasting?
链接：https://neurips.cc/virtual/2024/poster/94012
arXiv：https://arxiv.org/abs/2405.16877
作者：Dongbin Kim · Jinseong Park · Jaewook Lee · Hoki Kim
关键词：预测，交叉注意力
29 Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
链接：https://neurips.cc/virtual/2024/poster/96748
arXiv：https://arxiv.org/abs/2401.03955
代码：https://github.com/ibm-granite/granite-tsfm/tree/main/tsfm_public/models/tinytimemixer
Huggingface：https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1
作者：Vijay Ekambaram · Arindam Jati · Pankaj Dayama · Sumanta Mukherjee · Nam Nguyen · WESLEY M GIFFORD · Chandra Reddy · Jayant Kalagnanam
关键词：零样本/少样本预测
AI蜗牛车：IBM Research：轻量级时间序列大模型提升Few-shot Learning时序预测效果
30 SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series
链接：https://neurips.cc/virtual/2024/poster/94119
作者：Zhihao Dai · Ligang He · Shuanghua Yang · Matthew Leeke
关键词：异常检测，空间关联感知
31 Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
链接：https://neurips.cc/virtual/2024/poster/96819
作者：Ilan Naiman · Nimrod Berman · Itai Pemper · Idan Arbiv · Gal Fadlon · Omer Asher · Omri Azencot
关键词：分类（长时），判别（短时）
32 Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
链接：https://neurips.cc/virtual/2024/poster/93973
arXiv：https://arxiv.org/abs/2408.00041
作者：Junru Chen · Tianyu Cao · Jing Xu · Jiahe Li · Zhilong Chen · Tao Xiao · YANG YANG
关键词：分类
时序人：NeurIPS 2024 | 分段时序多分类任务下的一致性学习框架
33 Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
链接：https://neurips.cc/virtual/2024/poster/96575
作者：Qiang Wu · Gechang Yao · Zhixi Feng · Yang Shuyuan
关键词：分析，Transformer
34 Shape analysis for time series
链接：https://neurips.cc/virtual/2024/poster/95718
作者：Thibaut Germain · Samuel Gruffaz · Charles Truong · Alain Durmus · Laurent Oudre
关键词：分析，生理时序，无监督
35 UNITS: A Unified Multi-Task Time Series Model
链接：https://neurips.cc/virtual/2024/poster/93709
arXiv：https://arxiv.org/abs/2403.00131
代码：https://github.com/mims-harvard/UniTS
作者：Shanghua Gao · Teddy Koker · Owen Queen · Tom Hartvigsen · Theodoros Tsiligkaridis · Marinka Zitnik
关键词：多任务，基础模型
36 Large Pre-trained time series models for cross-domain Time series analysis tasks
链接：https://neurips.cc/virtual/2024/poster/93205
arXiv：https://arxiv.org/abs/2311.11413
代码：https://github.com/kage08/SegmentTS/
作者：Harshavardhan Prabhakar Kamarthi · B. Aditya Prakash
关键词：分析，跨域，预训练
37 Segment, Shuffle, and Stitch: A Simple Mechanism for Improving Time-Series Representations
链接：https://neurips.cc/virtual/2024/poster/92935
arXiv：https://arxiv.org/abs/2405.20082
代码：https://github.com/shivam-grover/S3-TimeSeries
作者：Shivam Grover · Amin Jalali · Ali Etemad
关键词：表示学习
38 Task-oriented Time Series Imputation Evaluation via Generalized Representers
链接：https://neurips.cc/virtual/2024/poster/93717
代码：https://github.com/hkuedl/Task-Oriented-Imputation
作者：Zhixian Wang · Linxiao Yang · Liang Sun · Qingsong Wen · Yi Wang
关键词：插补，评估方法
39 Exploiting Representation Curvature for Boundary Detection in Time Series
链接：https://neurips.cc/virtual/2024/poster/94837
作者：Yooju Shin · Jaehyun Park · Susik Yoon · Hwanjun Song · Byung Suk Lee · Jae-Gil Lee
关键词：边界检测
40 Learning diverse causally emergent representations from time series data
链接：https://neurips.cc/virtual/2024/poster/92973
作者：David McSharry · Christos Kaplanis · Fernando Rosas · Pedro A.M Mediano
关键词：因果涌现
41 SDformer: Similarity-driven Discrete Transformer For Time Series Generation
链接：https://neurips.cc/virtual/2024/poster/94642
作者：Zhicheng Chen · FENG SHIBO · Zhong Zhang · Xi Xiao · Xingyu Gao · Peilin Zhao
关键词：时间序列生成，离散Transformer
42 FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation
链接：https://neurips.cc/virtual/2024/poster/96595
作者：Asadullah Hill Galib · Pang-Ning Tan · Lifeng Luo
关键词：时间序列生成，条件扩散模型
43 ANT: Adaptive Noise Schedule for Time Series Diffusion Models
链接：https://neurips.cc/virtual/2024/poster/96850
作者：Seunghan Lee · Kibok Lee · Taeyoung Park
关键词：扩散模型，自适应噪声
44 Trajectory Flow Matching with Applications to Clinical Time Series Modelling
链接：https://neurips.cc/virtual/2024/poster/94212
作者：Xi (Nicole) Zhang · Yuan Pu · Yuki Kawamura · Andrew Loza · Yoshua Bengio · Dennis Shung · Alexander Tong
关键词：建模，临床时间序列，流匹配
45 Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
链接：https://neurips.cc/virtual/2024/poster/93680
arXiv：https://arxiv.org/abs/2406.04320
作者：Ali Behrouz · Michele Santacatterina · Ramin Zabih
关键词：建模，状态空间模型
46 Reinforced Cross-Domain Knowledge Distillation on Time Series Data
链接：https://neurips.cc/virtual/2024/poster/93330
作者：QING XU · Min Wu · Xiaoli Li · Kezhi Mao · Zhenghua Chen
关键词：知识蒸馏，无监督域适应
47 Boosting Transferability and Discriminability for Time Series Domain Adaptation
链接：https://neurips.cc/virtual/2024/poster/94429
作者：Mingyang Liu · Xinyang Chen · Yang Shu · Xiucheng Li · Weili Guan · Liqiang Nie
关键词：域适应，迁移性，判别性
48 Towards Editing Time Series
链接：https://neurips.cc/virtual/2024/poster/93468
作者：Baoyu Jing · Shuqi Gu · Tianyu Chen · Zhiyu Yang · Dongsheng Li · Jingrui He · Kan Ren
关键词：时间序列编辑，合成时间序列
49 Conformalized Time Series with Semantic Features
链接：https://neurips.cc/virtual/2024/poster/95653
作者：Baiting Chen · Zhimei Ren · Lu Cheng
关键词：共形预测，分布偏移
50 ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
链接：https://neurips.cc/virtual/2024/poster/93042
作者：Etienne Vareille · Michele Linardi · Vassilis Christophides · Ioannis Tsamardinos
关键词：时间序列选择
51 Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
链接：https://neurips.cc/virtual/2024/poster/93348
作者：Giangiacomo Mercatali · Andre Freitas · Jie Chen
关键词：不规则时间序列，因果，常微分方程
52 Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
链接：https://neurips.cc/virtual/2024/poster/94588
作者：Chengtao Jian · Kai Yang · Yang Jiao
关键词：分布外泛化，LLM
53 UniMTS: Unified Pre-training for Motion Time Series
链接：https://neurips.cc/virtual/2024/poster/96073
作者：Xiyuan Zhang · Diyan Teng · Ranak Roy Chowdhury · Shuheng Li · Dezhi Hong · Rajesh Gupta · Jingbo Shang
关键词：运动时间序列，预训练
54 Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
链接：https://neurips.cc/virtual/2024/poster/93940
arXiv：https://arxiv.org/abs/2405.19363
代码：https://github.com/DL4mHealth/Medformer
作者：Yihe Wang · Nan Huang · Taida Li · Yujun Yan · Xiang Zhang
关键词：分类，医疗时间序列
55 Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification
链接：https://neurips.cc/virtual/2024/poster/93522
作者：Yunshi Wen · Tengfei Ma · Lily Weng · Lam Nguyen · Anak Agung Julius
关键词：分类，可解释性，泛化性
D&B Track
56 IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
链接：https://neurips.cc/virtual/2024/poster/97776
arXiv：https://arxiv.org/abs/2405.16069
代码：https://github.com/Healthy-AI/IncomeSCM
作者：Fredrik Johansson（独立作者）
关键词：因果估计，模拟器
57 The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark
链接：https://neurips.cc/virtual/2024/poster/97690
代码：https://github.com/TheDatumOrg/TSB-AD
作者：Qinghua Liu · John Paparrizos
关键词：异常检测，benchmark
58 Building Timeseries Dataset: Empowering Large-Scale Building Analytics
链接：https://neurips.cc/virtual/2024/poster/97839
arXiv：https://arxiv.org/abs/2406.08990
代码：https://github.com/cruiseresearchgroup/DIEF_BTS
作者：Arian Prabowo · Xiachong LIN · Imran Razzak · Hao Xue · Emily Yap · Matthew Amos · Flora Salim
关键词：建筑时间序列，数据集，
59 Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
链接：https://neurips.cc/virtual/2024/poster/97582
arXiv：https://arxiv.org/abs/2406.08627
library代码：https://github.com/AdityaLab/MM-TSFlib
dataset 代码：https://github.com/AdityaLab/Time-MMD
作者：Haoxin Liu · Shangqing Xu · Zhiyuan Zhao · Lingkai Kong · Harshavardhan Prabhakar Kamarthi · Aditya Sasanur · Megha Sharma · Jiaming Cui · Qingsong Wen · Chao Zhang · B. Aditya Prakash
关键词：数据集，分析，多模态，多域
60 TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
链接：https://neurips.cc/virtual/2024/poster/97532
arXiv：https://arxiv.org/abs/2305.11567
作者：Alexander Nikitin · Letizia Iannucci · Samuel Kaski
关键词：时间序列生成，合成时间序列，框架
60 TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
链接：https://neurips.cc/virtual/2024/poster/97532
arXiv：https://arxiv.org/abs/2305.11567
作者：Alexander Nikitin · Letizia Iannucci · Samuel Kaski
关键词：时间序列生成，合成时间序列，框架
61 ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons
链接：https://neurips.cc/virtual/2024/poster/97527
arXiv：https://arxiv.org/abs/2310.07446
代码：https://github.com/microsoft/ProbTS
作者：Jiawen Zhang · Xumeng Wen · Zhenwei Zhang · Shun Zheng · Jia Li · Jiang Bian
关键词：概率预测，benchmark
相关链接
NeurIPS 24 Accepted Papers：https://neurips.cc/virtual/2024/papers.html
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