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60篇，其中正会56篇，D&B Track4篇

预测：1-29

异常检测：30，58

分类：32，55，56

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

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1. Retrieval-Augumented Diffusion Models for Time Series Forecasting
2. Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective
3. Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
4. FilterNet: Harnessing Frequency Filters for Time Series Forecasting
5. Frequency Adaptive Normalization For Non-stationary Time Series Forecasting
6. Rethinking the Power of Timestamps for Robust Time Series Forecasting: A Global-Local Fusion Perspective
7. AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
8. DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting
9. BackTime: Backdoor Attacks on Multivariate Time Series Forecasting
10. Are Language Models Actually Useful for Time Series Forecasting?
11. Rethinking Fourier Transform for Long-term Time Series Forecasting: A Basis Functions Perspective
12. Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
13. Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting
14. Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics
15. DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching
16. Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
17. PGN: The RNN’s New Successor is Effective for Long-Range Time Series Forecasting
18. SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
19. Multivariate Probabilistic Time Series Forecasting with Correlated Errors
20. CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
21. Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
22. CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
23. Scaling Law for Time Series Forecasting
24. From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection
25. From Similarity to Superiority: Channel Clustering for Time Series Forecasting
26. TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
27. ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
28. Are Self-Attentions Effective for Time Series Forecasting?
29. Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
30. SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series
31. Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series
32. Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
33. Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis
34. Shape analysis for time series
35. UNITS: A Unified Multi-Task Time Series Model
36. Large Pre-trained time series models for cross-domain Time series analysis tasks
37. “Segment, Shuffle, and Stitch: A Simple Mechanism for Improving Time-Series Representations”
38. Task-oriented Time Series Imputation Evaluation via Generalized Representers
39. Exploiting Representation Curvature for Boundary Detection in Time Series
40. Learning diverse causally emergent representations from time series data
41. SDformer: Similarity-driven Discrete Transformer For Time Series Generation
42. FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation
43. ANT: Adaptive Noise Schedule for Time Series Diffusion Models
44. Trajectory Flow Matching with Applications to Clinical Time Series Modelling
45. Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
46. Reinforced Cross-Domain Knowledge Distillation on Time Series Data
47. Boosting Transferability and Discriminability for Time Series Domain Adaptation
48. Towards Editing Time Series
49. Conformalized Time Series with Semantic Features
50. ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions
51. Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
52. Tri-Level Navigator: LLM-Empowered Tri-Level Learning for Time Series OOD Generalization
53. UniMTS: Unified Pre-training for Motion Time Series
54. IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
55. Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
56. Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification

D&B Track

57. The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark
58. Building Timeseries Dataset: Empowering Large-Scale Building Analytics
59. Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
60. TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series

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

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。

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/2402.01000

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

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

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

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

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

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 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

55 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

56 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

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，metadata

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

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

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

[外链图片转存中…(img-ZWL3OAEG-1729847739055)]

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

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