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这半年一直在看时间序列相关的论文,这篇汇总非常有用,全是干货。建议收藏!
【搬运自[github]】【侵删】
A professionally curated list of papers (with available code), tutorials, and surveys on recent AI for Time Series Analysis (AI4TS), including Time Series, Spatio-Temporal Data, Event Data, Sequence Data, Temporal Point Processes, etc., at the Top AI Conferences and Journals, which is updated ASAP (the earliest time) once the accepted papers are announced in the corresponding top AI conferences/journals. Hope this list would be helpful for researchers and engineers who are interested in AI for Time Series Analysis.
【译文】专业策划的关于最近AI用于时间序列分析(AI4TS)的论文列表(有可用代码),教程和调查,包括时间序列,时空数据,事件数据,序列数据,时间点过程等,在顶级AI会议和期刊上,一旦被接受的论文在相应的顶级AI会议/期刊上公布,就会尽快更新(最早的时间)。希望这个列表对对时间序列分析的人工智能感兴趣的研究人员和工程师有所帮助。
The top conferences including:
The top journals including (mainly for survey papers):
CACM, PIEEE, TPAMI, TKDE, TNNLS, TITS, TIST, SPM, JMLR, JAIR, CSUR, DMKD, KAIS, IJF, arXiv(selected), etc.
If you find any missed resources (paper/code) or errors, please feel free to open an issue or make a pull request.
For general Recent AI Advances: Tutorials and Surveys in various areas (DL, ML, DM, CV, NLP, Speech, etc.) at the Top AI Conferences and Journals, please check This Repo.
Dynamic Multi-Network Mining of Tensor Time Series [paper]
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series [paper]
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learning [paper]
GraFITi: Graphs for Forecasting Irregularly Sampled Time Series [paper]
IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solvers [paper]
SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation [paper]
CGS-Mask: Making Time Series Predictions Intuitive for All [paper]
CUTS+: High-dimensional Causal Discovery from Irregular Time-series [paper]
Fully-Connected Spatial-Temporal Graph for Multivariate Time Series Data [paper]
FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [paper] [official code]
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [paper] [official code]
Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting [paper]
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [paper]
Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement
Learning Latent Seasonal-Trend Representations for Time Series Forecasting
WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting
Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
Meta-Learning Dynamics Forecasting Using Task Inference [paper]
Conformal Prediction with Temporal Quantile Adjustments
Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency, [paper] [official code]
Causal Disentanglement for Time Series
BILCO: An Efficient Algorithm for Joint Alignment of Time Series
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Efficient learning of nonlinear prediction models with time-series privileged information
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
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