最新-时间序列-顶会顶刊论文汇总【论文/源码链接】

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AI for Time Series (AI4TS) Papers, Tutorials, and Surveys

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:

Machine Learning: NeurIPS, ICML, ICLR
Data Mining: KDD, WWW
Artificial Intelligence: AAAI, IJCAI
Data Management: SIGMOD, VLDB, ICDE
Misc (selected): AISTAT, CIKM, ICDM, WSDM, SIGIR, ICASSP, CVPR, ICCV, etc.

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.

Main Recent Update Note

[Mar. 04, 2024] Add papers accepted by ICLR’24, AAAI’24, WWW’24!
[Jul. 05, 2023] Add papers accepted by KDD’23!
[Jun. 20, 2023] Add papers accepted by ICML’23!
[Feb. 07, 2023] Add papers accepted by ICLR’23 and AAAI’23!
[Sep. 18, 2022] Add papers accepted by NeurIPS’22!
[Jul. 14, 2022] Add papers accepted by KDD’22!
[Jun. 02, 2022] Add papers accepted by ICML’22, ICLR’22, AAAI’22, IJCAI’22!

AI4TS Tutorials and Surveys

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* AI4TS Tutorials
* AI4TS Surveys

AI4TS Papers 2024

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* NeurIPS 2024, ICML 2024, ICLR 2024
* KDD 2024, WWW 2024, AAAI 2024, IJCAI 2024
* SIGMOD VLDB ICDE 2024
* Misc 2024

AI4TS Papers 2023

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* NeurIPS 2023, ICML 2023, ICLR 2023
* KDD 2023, AAAI 2023, IJCAI 2023
* SIGMOD VLDB ICDE 2023
* Misc 2023

AI4TS Papers 2022

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* NeurIPS 2022, ICML 2022, ICLR 2022
* KDD 2022, AAAI 2022, IJCAI 2022
* SIGMOD VLDB ICDE 2022
* Misc 2022

AI4TS Papers 2021

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* NeurIPS 2021, ICML 2021, ICLR 2021
* KDD 2021, AAAI 2021, IJCAI 2021
* SIGMOD VLDB ICDE 2021
* Misc 2021

AI4TS Papers 201X-2020 Selected

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* NeurIPS 201X-2020, ICML 201X-2020, ICLR 201X-2020
* KDD 201X-2020, AAAI 201X-2020, IJCAI 201X-2020
* SIGMOD VLDB ICDE 201X-2020
* Misc 201X-2020

AI4TS Tutorials and Surveys

AI4TS Tutorials

Out-of-Distribution Generalization in Time Series, in AAAI 2024. [Link]
Robust Time Series Analysis and Applications: An Interdisciplinary Approach, in ICDM 2023. [Link]
Robust Time Series Analysis and Applications: An Industrial Perspective, in KDD 2022. [Link]
Time Series in Healthcare: Challenges and Solutions, in AAAI 2022. [Link]
Time Series Anomaly Detection: Tools, Techniques and Tricks, in DASFAA 2022. [Link]
Modern Aspects of Big Time Series Forecasting, in IJCAI 2021. [Link]
Explainable AI for Societal Event Predictions: Foundations, Methods, and Applications, in AAAI 2021. [Link]
Physics-Guided AI for Large-Scale Spatiotemporal Data, in KDD 2021. [Link]
Deep Learning for Anomaly Detection, in KDD & WSDM 2020. [Link1] [Link2] [Link3]
Building Forecasting Solutions Using Open-Source and Azure Machine Learning, in KDD 2020. [Link]
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data, KDD 2020. [Link]
Forecasting Big Time Series: Theory and Practice, KDD 2019. [Link]
Spatio-Temporal Event Forecasting and Precursor Identification, KDD 2019. [Link]
Modeling and Applications for Temporal Point Processes, KDD 2019. [Link1] [Link2]

AI4TS Surveys

General Time Series Survey

What Can Large Language Models Tell Us about Time Series Analysis, in arXiv 2024. [paper]
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook, in arXiv 2023. [paper] [Website]
Deep Learning for Multivariate Time Series Imputation: A Survey, in arXiv 2024. [paper] [Website]
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in arXiv 2023. [paper] [Website]
Transformers in Time Series: A Survey, in IJCAI 2023. [paper] [GitHub Repo]
Time series data augmentation for deep learning: a survey, in IJCAI 2021. [paper]
Neural temporal point processes: a review, in IJCAI 2021. [paper]
Causal inference for time series analysis: problems, methods and evaluation, in KAIS 2022. [paper]
Survey and Evaluation of Causal Discovery Methods for Time Series, in JAIR 2022. [paper]
Deep learning for spatio-temporal data mining: A survey, in TKDE 2020. [paper]
Generative Adversarial Networks for Spatio-temporal Data: A Survey, in TIST 2022. [paper]
Spatio-Temporal Data Mining: A Survey of Problems and Methods, in CSUR 2018. [paper]
A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series, in NeurIPS Workshop 2020. [paper]
Count Time-Series Analysis: A signal processing perspective, in SPM 2019. [paper]
Wavelet transform application for/in non-stationary time-series analysis: a review, in Applied Sciences 2019. [paper]
Granger Causality: A Review and Recent Advances, in Annual Review of Statistics and Its Application 2014. [paper]
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data, in arXiv 2020. [paper]
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data, in arXiv 2022. [paper]
A Survey on Time-Series Pre-Trained Models, in arXiv 2023. [paper] [link]
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects, in arXiv 2023. [paper] [Website]
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection, in arXiv 2023. [paper] [Website]

Time Series Forecasting Survey

Forecasting: theory and practice, in IJF 2022. [paper]
Time-series forecasting with deep learning: a survey, in Philosophical Transactions of the Royal Society A 2021. [paper]
Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions, in TITS 2022. [paper]
Event prediction in the big data era: A systematic survey, in CSUR 2022. [paper]
A brief history of forecasting competitions, in IJF 2020. [paper]
Neural forecasting: Introduction and literature overview, in arXiv 2020. [paper]
Probabilistic forecasting, in Annual Review of Statistics and Its Application 2014. [paper]

Time Series Anomaly Detection Survey

A review on outlier/anomaly detection in time series data, in CSUR 2021. [paper]
Anomaly detection for IoT time-series data: A survey, in IEEE Internet of Things Journal 2019. [paper]
A Survey of AIOps Methods for Failure Management, in TIST 2021. [paper]
Sequential (quickest) change detection: Classical results and new directions, in IEEE Journal on Selected Areas in Information Theory 2021. [paper]
Outlier detection for temporal data: A survey, TKDE’13. [paper]
Anomaly detection for discrete sequences: A survey, TKDE’12. [paper]
Anomaly detection: A survey, CSUR’09. [paper]

Time Series Classification Survey

Deep learning for time series classification: a review, in Data Mining and Knowledge Discovery 2019. [paper]
Approaches and Applications of Early Classification of Time Series: A Review, in IEEE Transactions on Artificial Intelligence 2020. [paper]

[paper]

AI4TS Papers 2024

NeurIPS 2024

ICML 2024

ICLR 2024

Time Series Forecasting

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [paper] [official code]
TEST: Text Prototype Aligned Embedding to Activate LLM’s Ability for Time Series [paper] [official code]
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting [paper]
DAM: A Foundation Model for Forecasting [paper]
CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [paper] [official code]
Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting [paper] [official code]
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [paper] [official code]
GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings [paper]
Transformer-Modulated Diffusion Models for Probabilistic Multivariate Time Series Forecasting [paper]
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies [paper] [official code]
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis [paper]
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting [paper]
FITS: Modeling Time Series with 10k Parameters [paper]
Multi-Resolution Diffusion Models for Time Series Forecasting [paper]
MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process [paper]
Interpretable Sparse System Identification: Beyond Recent Deep Learning Techniques on Time-Series Prediction [paper]
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [paper]
Towards Transparent Time Series Forecasting [paper]
Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values [paper]
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators [paper]
VQ-TR: Vector Quantized Attention for Time Series Forecasting [paper]
Copula Conformal prediction for multi-step time series prediction [paper]
ClimODE: Climate Forecasting With Physics-informed Neural ODEs [paper]
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction [paper]
T-Rep: Representation Learning for Time Series using Time-Embeddings [paper]
Periodicity Decoupling Framework for Long-term Series Forecasting [paper]
Self-Supervised Contrastive Forecasting [paper]

Others

Explaining Time Series via Contrastive and Locally Sparse Perturbations [paper] [official code]
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery [paper] [official code]
SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series [paper]
Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns [paper]
Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data [paper]
Soft Contrastive Learning for Time Series [paper]
Retrieval-Based Reconstruction For Time-series Contrastive Learning [paper]
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach [paper]
Diffusion-TS: Interpretable Diffusion for General Time Series Generation [paper]
Disentangling Time Series Representations via Contrastive based l-Variational Inference [paper]
Leveraging Generative Models for Unsupervised Alignment of Neural Time Series Data [paper]
Conditional Information Bottleneck Approach for Time Series Imputation [paper]
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs [paper]
Learning to Embed Time Series Patches Independently [paper]
Parametric Augmentation for Time Series Contrastive Learning [paper]
Inherently Interpretable Time Series Classification via Multiple Instance Learning [paper]

KDD 2024

WWW 2024

Time Series Forecasting

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting [paper]
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations [paper]

Time Series Anomaly Detection

LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection [paper]
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective [paper]
Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection [paper]

Others

Dynamic Multi-Network Mining of Tensor Time Series [paper]

E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series [paper]

AAAI 2024

Time Series Forecasting

U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting [paper]
HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting [paper]
Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting [paper]
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting [paper]
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting [paper]
Latent Diffusion Transformer for Probabilistic Time Series Forecasting [paper]
Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting [paper]

Time Series Classification, Clustering, Anomaly Detection

Graph-Aware Contrasting for Multivariate Time-Series Classification [paper]
Diffusion Language-Shapelets for Semisupervised Time-series Classification [paper]
Energy-efficient Streaming Time Series Classification with Attentive Power Iteration [paper]
Cross-Domain Contrastive Learning for Time Series Clustering [paper]
When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detection [paper]

Others

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]

AI4TS Papers 2023

NeurIPS 2023

Time Series Forecasting

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling [paper]
One Fits All: Power General Time Series Analysis by Pretrained LM [paper]
Large Language Models Are Zero Shot Time Series Forecasters [paper]
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis [paper]
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling [paper]
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective [paper]
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting [paper]
Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective [paper]
WITRAN: Water-wave Information Transmission and Recurrent Acceleration Network for Long-range Time Series Forecasting [paper]
Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting [paper]
Conformal PID Control for Time Series Prediction [paper]
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling [paper]
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors [paper]

Time Series Anomaly Detection, Classification

Drift doesn’t Matter: Dynamic Decomposition with Diffusion Reconstruction for Unstable Multivariate Time Series Anomaly Detection [paper]
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction [paper]
MEMTO: Memory-guided Transformer for Multivariate Time Series Anomaly Detection [paper]
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series [paper]
Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels [paper]

Others

Causal Discovery from Subsampled Time Series with Proxy Variables [paper]
Causal Discovery in Semi-Stationary Time Series [paper]
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency [paper]
Sparse Deep Learning for Time Series Data: Theory and Applications [paper]
CrossGNN: Confronting Noisy Multivariate Time Series Via Cross Interaction Refinement [paper]
WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction [paper]
Conformal Prediction for Time Series with Modern Hopfield Networks [paper]
Time Series Kernels based on Nonlinear Vector AutoRegressive Delay Embeddings [paper]
On the Constrained Time-Series Generation Problem [paper]
Contrast Everything: Multi-Granularity Representation Learning for Medical Time-Series [paper]
Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning [paper]
FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space [paper]
BioMassters: A Benchmark Dataset for Forest Biomass Estimation using Multi-modal Satellite Time-series [[paper]](https://neurips.cc/virtual/2023/poster/73499

ICML 2023

Time Series Forecasting

Learning Deep Time-index Models for Time Series Forecasting [paper]
Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts [paper]
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting [paper]
Feature Programming for Multivariate Time Series Prediction [paper]
Non-autoregressive Conditional Diffusion Models for Time Series Prediction [paper]

Time Series Anomaly Detection, Classification, Imputation, and XAI

Prototype-oriented unsupervised anomaly detection for multivariate time series [paper]
Probabilistic Imputation for Time-series Classification with Missing Data [paper]
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation [paper]
Self-Interpretable Time Series Prediction with Counterfactual Explanations [paper]
Learning Perturbations to Explain Time Series Predictions [paper]

Other Time Series Analysis

Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion [paper]
Neural Stochastic Differential Games for Time-series Analysis [paper]
Sequential Monte Carlo Learning for Time Series Structure Discovery [paper]
Context Consistency Regularization for Label Sparsity in Time Series [paper]
Sequential Predictive Conformal Inference for Time Series [paper]
Improved Online Conformal Prediction via Strongly Adaptive Online Learning [paper]
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series [paper]
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series [paper]
Domain Adaptation for Time Series Under Feature and Label Shifts [paper]
Deep Latent State Space Models for Time-Series Generation [paper]
Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series [paper]
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting [paper]
Generalized Teacher Forcing for Learning Chaotic Dynamics [paper]
Learning the Dynamics of Sparsely Observed Interacting Systems [paper]
Markovian Gaussian Process Variational Autoencoders [paper]
ClimaX: A foundation model for weather and climate [paper]

ICLR 2023

Time Series Forecasting

A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [paper] [official code]
Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting [paper] [official code]
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series Forecasting [paper] [official code]
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting [paper] [official code]
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting [paper] [official code]
Learning Fast and Slow for Time Series Forecasting [paper] [official code]
Koopman Neural Operator Forecaster for Time-series with Temporal Distributional Shifts [paper] [official code]
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms [paper] [official code]

Time Series Anomaly Detection and Classification

Unsupervised Model Selection for Time Series Anomaly Detection [paper] [official code]
Out-of-distribution Representation Learning for Time Series Classification [paper] [official code]

Other Time Series Analysis

Effectively Modeling Time Series with Simple Discrete State Spaces [paper] [official code]
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [paper] [official code]
Contrastive Learning for Unsupervised Domain Adaptation of Time Series [paper] [official code]
Recursive Time Series Data Augmentation [paper] [official code]
Multivariate Time-series Imputation with Disentangled Temporal Representations [paper] [official code]
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths [paper] [official code]
Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise [paper] [official code]
CUTS: Neural Causal Discovery from Unstructured Time-Series Data [paper] [official code]
Temporal Dependencies in Feature Importance for Time Series Prediction [paper] [official code]

KDD 2023

Time Series Anomaly Detection

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [paper] [official code]
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models [paper] [official code]
Precursor-of-Anomaly Detection for Irregular Time Series [paper]

Time Series Forecasting

When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting [paper]
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
Sparse Binary Transformers for Multivariate Time Series Modeling [paper] [official code]
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting

Time Series Forecasting (Traffic)

Frigate: Frugal Spatio-temporal Forecasting on Road Networks [paper] [official code]
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities
Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
Pattern Expansion and Consolidation on Evolving Graphs for Continual Traffic Prediction

Time Series Imputation

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data [paper] [official code]
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series

Others

Online Few-Shot Time Series Classification for Aftershock Detection [paper] [official code]
Self-supervised Classification of Clinical Multivariate Time Series using Time Series Dynamics
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Parameter-free Spikelet: Discovering Different Length and Warped Time Series Motifs using an Adaptive Time Series Representation
FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework
WHEN: A Wavelet-DTW Hybrid Attention Network for Heterogeneous Time Series Analysis

AAAI 2023

Time Series Forecasting

AirFormer: Predicting Nationwide Air Quality in China with Transformers [paper] [official code]
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting [paper] [official code]
WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series [paper] [official code]
Are Transformers Effective for Time Series Forecasting [paper] [official code]
Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose [paper] [official code]
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks [paper] [official code]
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting [paper] [official code]

Other Time Series Analysis

Temporal-Frequency Co-Training for Time Series Semi-Supervised Learning [paper] [official code]
SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation [paper] [official code]
Causal Recurrent Variational Autoencoder for Medical Time Series Generation [paper] [official code]
AEC-GAN: Adversarial Error Correction GANs for Auto-Regressive Long Time-series Generation [paper] [official code]
SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification [paper] [official code]

AI4TS Papers 2022

NeurIPS 2022

Time Series Forecasting

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

Other Time Series Analysis

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

ICML 2022

Time Series Forecasting

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting [paper] [official code]
TACTiS: Transformer-Attentional Copulas for Time Series [paper] [official code]
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes [paper] [official code]
Domain Adaptation for Time Series Forecasting via Attention Sharing [paper]
DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting [paper] [official code]

Time Series Anomaly Detection

Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection [paper]

Other Time Series Analysis

Adaptive Conformal Predictions for Time Series [paper] [official code]
Modeling Irregular Time Series with Continuous Recurrent Units [paper] [official code]
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion [paper]
Reconstructing nonlinear dynamical systems from multi-modal time series [paper] [official code]
Utilizing Expert Features for Contrastive Learning of Time-Series Representations [paper] [official code]
Learning of Cluster-based Feature Importance for Electronic Health Record Time-series [paper]

ICLR 2022

Time Series Forecasting

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting [paper] [official code]
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting [paper] [official code]
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting [paper] [official code]
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift [paper] [official code]
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting [paper] [official code]
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [paper] [official code]
On the benefits of maximum likelihood estimation for Regression and Forecasting [paper]
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting [paper] [official code]

Time Series Anomaly Detection

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [paper] [official code]
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series [paper] [official code]

Time Series Classification

T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis [paper]
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification [paper]

Other Time Series Analysis

Graph-Guided Network for Irregularly Sampled Multivariate Time Series [paper]
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series [paper]
Transformer Embeddings of Irregularly Spaced Events and Their Participants [paper]
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks [paper]
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series [paper]
Huber Additive Models for Non-stationary Time Series Analysis [paper]
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations [paper]
Imbedding Deep Neural Networks [paper]
Coherence-based Label Propagation over Time Series for Accelerated Active Learning [paper]
Long Expressive Memory for Sequence Modeling [paper]
Autoregressive Quantile Flows for Predictive Uncertainty Estimation [paper]
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks [paper]
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification [paper]
Explaining Point Processes by Learning Interpretable Temporal Logic Rules [paper]

KDD 2022

Time Series Forecasting

Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting [

复制代码

](https://github.com/DAMO-DI-ML/KDD2022-Quatformer)
  * Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting
  * Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
  * Multi-Variate Time Series Forecasting on Variable Subset
  * Greykite: Deploying Flexible Forecasting at Scale in LinkedIn

##### Time Series Anomaly Detection

  * Local Evaluation of Time Series Anomaly Detection Algorithms
  * Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams

##### Other Time-Series/Spatio-Temporal Analysis

  * Task-Aware Reconstruction for Time-Series Transformer
  * Towards Learning Disentangled Representations for Time Series
  * ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
  * Non-stationary Time-aware Kernelized Attention for Temporal Event Prediction
  * MSDR: Multi-Step Dependency Relation Networks for Spatial Temporal Forecasting
  * Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction
  * Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models
  * Robust Event Forecasting with Spatiotemporal Confounder Learning
  * Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning
  * Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer
  * Characterizing Covid waves via spatio-temporal decomposition

#### AAAI 2022

##### Time Series Forecasting

  * CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting [[paper]](https://aaai-2022.virtualchair.net/poster_aaai7403)
  * Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting [[paper]](https://aaai-2022.virtualchair.net/poster_aaai8424)
  * DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation [[paper]](https://arxiv.org/abs/2201.04038) [official code]](https://github.com/microsoft/qlib/tree/main/examples/benchmarks_dynamic/DDG-DA)
  * PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model [[paper]](https://aaai-2022.virtualchair.net/poster_aisi7128)
  * LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to  
Electricity Smart Meter Data [[paper]](https://aaai-2022.virtualchair.net/poster_aisi8802)

  * Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration [[paper]](https://arxiv.org/abs/2110.05196) [[official code]](https://github.com/Sijie-umn/SSF-MIP)
  * CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting [[paper]](https://aaai-2022.virtualchair.net/poster_aisi6475)
  * Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting [[paper]](https://arxiv.org/abs/2101.01000) [[official code]](https://github.com/bird-tao/clcrn)
  * Graph Neural Controlled Differential Equations for Traffic Forecasting [[paper]](https://aaai-2022.virtualchair.net/poster_aaai6502) [[official code]](https://github.com/jeongwhanchoi/STG-NCDE)
  * STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction [[paper]](https://aaai-2022.virtualchair.net/poster_aaai211) [[official code]](https://github.com/Echo-Ji/STDEN)

##### Time Series Anomaly Detection

  * Towards a Rigorous Evaluation of Time-Series Anomaly Detection [[paper]](https://aaai-2022.virtualchair.net/poster_aaai2239)
  * AnomalyKiTS-Anomaly Detection Toolkit for Time Series [[Demo paper]](https://aaai-2022.virtualchair.net/poster_dm318)

##### Other Time Series Analysis

  * TS2Vec: Towards Universal Representation of Time Series [[paper]](https://aaai-2022.virtualchair.net/poster_aaai8809) [[official code]](https://github.com/yuezhihan/ts2vec)
  * I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding [[paper]](https://aaai-2022.virtualchair.net/poster_aaai10930)
  * Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis [[paper]](https://aaai-2022.virtualchair.net/poster_aaai4151)
  * Conditional Loss and Deep Euler Scheme for Time Series Generation [[paper]](https://aaai-2022.virtualchair.net/poster_aaai12878)
  * Clustering Interval-Censored Time-Series for Disease Phenotyping [[paper]](https://aaai-2022.virtualchair.net/poster_aaai12517)

#### IJCAI 2022

##### Time Series Forecasting

  * Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting [[paper]](https://arxiv.org/abs/2204.13767)
  * Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts [[paper]](https://arxiv.org/abs/2111.03394) [[official code]](https://github.com/pratham16cse/aggforecaster)
  * Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
  * DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data [[paper]](https://arxiv.org/abs/2205.02441) [[official code]](https://github.com/galib19/deepextrema-ijcai22-)
  * Memory Augmented State Space Model for Time Series Forecasting
  * Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data
  * Long-term Spatio-Temporal Forecasting via Dynamic Multiple-Graph Attention [[paper]](https://arxiv.org/abs/2204.11008) [[official code]](https://arxiv.org/abs/2204.11008)
  * FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting

##### Time Series Anomaly Detection

  * Neural Contextual Anomaly Detection for Time Series [[paper]](https://arxiv.org/abs/2107.07702)
  * GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning

##### Time Series Classification

  * A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification [[paper]](https://cpsl.pratt.duke.edu/sites/cpsl.pratt.duke.edu/files/docs/gao_ijcai22.pdf)
  * T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification

#### SIGMOD VLDB ICDE 2022

##### Time Series Forecasting

  * METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting, VLDB’22. [[paper]](http://vldb.org/pvldb/vol15/p224-cui.pdf) [[official code]](https://zheng-kai.com/code/metro_single_s.zip)
  * AutoCTS: Automated Correlated Time Series Forecasting, VLDB’22. [[paper]](http://vldb.org/pvldb/vol15/p971-wu.pdf)
  * Towards Spatio-Temporal Aware Traffic Time Series Forecasting, ICDE’22. [[paper]](https://arxiv.org/abs/2203.15737) [[official code]](https://github.com/razvanc92/st-wa)

##### Time Series Anomaly Detection

  * Sintel: A Machine Learning Framework to Extract Insights from Signals, SIGMOD’22. [[paper]](https://arxiv.org/abs/2204.09108) [[official code]](https://github.com/sarahmish/sintel-paper)
  * TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection, VLDB’22. [[paper]](https://helios2.mi.parisdescartes.fr/~themisp/publications/pvldb22-tsbuad.pdf) [[official code]](https://github.com/johnpaparrizos/TSB-UAD)
  * TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, VLDB’22. [[paper]](https://arxiv.org/abs/2201.07284) [[official code]](https://github.com/imperial-qore/tranad)
  * Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles, VLDB’22. [[paper]](http://vldb.org/pvldb/vol15/p611-chaves.pdf)
  * Robust and Explainable Autoencoders for Time Series Outlier Detection, ICDE’22. [[paper]](https://arxiv.org/abs/2204.03341)
  * Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders, ICDE’22.

##### Time Series Classification

  * IPS: Instance Profile for Shapelet Discovery for Time Series Classification, ICDE’22. [[paper]](https://personal.ntu.edu.sg/assourav/papers/ICDE-22-IPS.pdf)
  * Towards Backdoor Attack on Deep Learning based Time Series Classification, ICDE’22. [[paper]]()

##### Other Time Series Analysis

  * OnlineSTL: Scaling Time Series Decomposition by 100x, VLDB’22. [[paper]](http://vldb.org/pvldb/vol15/p1417-mishra.pdf)
  * Efficient temporal pattern mining in big time series using mutual information, VLDB’22. [[paper]](https://arxiv.org/abs/2010.03653)
  * Learning Evolvable Time-series Shapelets, ICDE’22.

#### Misc 2022

##### Time Series Forecasting

  * CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting, WWW’22. [[paper]](https://arxiv.org/abs/2109.07438) [[official code]](https://github.com/adityalab/camul)
  * Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction, WWW’22. [[paper]](https://web.archive.org/web/20220426115606id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512056)
  * RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph, WWW’22. [[paper]](https://dl.acm.org/doi/abs/10.1145/3485447.3511974)
  * Robust Probabilistic Time Series Forecasting, AISTATS’22. [[paper]](https://arxiv.org/abs/2202.11910) [[official code]](https://github.com/tetrzim/robust-probabilistic-forecasting)
  * Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting, AISTATS’22. [[paper]](https://arxiv.org/abs/2111.06581)

##### Time Series Anomaly Detection

  * TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis, CIKM’22. [[paper]](https://arxiv.org/abs/2210.09693) [[official code]](https://github.com/DAMO-DI-ML/CIKM22-TFAD)
  * Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection, AISTATS’22. [[paper]](https://arxiv.org/abs/2202.07586) [[official code]](https://github.com/cchallu/dghl)
  * A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems, WWW’22. [[paper]](https://dl.acm.org/doi/10.1145/3485447.3511984)

##### Other Time Series Analysis

  * Decoupling Local and Global Representations of Time Series, AISTATS’22. [[paper]](https://arxiv.org/abs/2202.02262) [[official code]](https://github.com/googleinterns/local_global_ts_representation)
  * LIMESegment: Meaningful, Realistic Time Series Explanations, AISTATS’22. [[paper]](https://proceedings.mlr.press/v151/sivill22a.html)
  * Using time-series privileged information for provably efficient learning of prediction models, AISTATS’22. [[paper]](https://arxiv.org/abs/2110.14993) [[official code]](https://github.com/RickardKarl/LearningUsingPrivilegedTimeSeries)
  * Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation, AISTATS’22. [[paper]]() [[official code]](https://arxiv.org/abs/2202.11585)
  * EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting, WWW’22. [[paper]](https://arxiv.org/abs/2204.08771)

### AI4TS Papers 2021

#### NeurIPS 2021

##### Time Series Forecasting

  * Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [[paper]](https://arxiv.org/abs/2106.13008) [[official code]](https://github.com/thuml/autoformer)
  * MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data [[paper]](https://arxiv.org/abs/2110.14354)
  * Conformal Time-Series Forecasting [[paper]](https://proceedings.neurips.cc/paper/2021/hash/312f1ba2a72318edaaa995a67835fad5-Abstract.html) [[official code]](https://github.com/kamilest/conformal-rnn)
  * Probabilistic Forecasting: A Level-Set Approach [[paper]](https://proceedings.neurips.cc/paper/2021/hash/32b127307a606effdcc8e51f60a45922-Abstract.html)
  * Topological Attention for Time Series Forecasting [[paper]](https://arxiv.org/abs/2107.09031)
  * When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [[paper]](https://arxiv.org/abs/2106.03904) [[official code]](https://github.com/AdityaLab/EpiFNP)
  * Monash Time Series Forecasting Archive [[paper]](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/eddea82ad2755b24c4e168c5fc2ebd40-Abstract-round2.html) [[official code]](https://forecastingdata.org/)

##### Time Series Anomaly Detection

  * Revisiting Time Series Outlier Detection: Definitions and Benchmarks [[paper]](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/ec5decca5ed3d6b8079e2e7e7bacc9f2-Abstract-round1.html) [[official code]](https://github.com/datamllab/tods/tree/benchmark)
  * Online false discovery rate control for anomaly detection in time series [[paper]](https://arxiv.org/abs/2112.03196)
  * Detecting Anomalous Event Sequences with Temporal Point Processes [[paper]](https://arxiv.org/abs/2106.04465)

##### Other Time Series Analysis

  * Probabilistic Transformer For Time Series Analysis [[paper]](https://proceedings.neurips.cc/paper/2021/hash/c68bd9055776bf38d8fc43c0ed283678-Abstract.html)
  * Shifted Chunk Transformer for Spatio-Temporal Representational Learning [[paper]](https://arxiv.org/abs/2108.11575)
  * Deep Explicit Duration Switching Models for Time Series [[paper]](https://openreview.net/forum?id=LaM6G4yrMy0) [[official code]](https://github.com/abdulfatir/REDSDS)
  * Time-series Generation by Contrastive Imitation [[paper]](https://openreview.net/forum?id=RHZs3GqLBwg)
  * CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [[paper]](https://arxiv.org/abs/2107.03502) [[official code]](https://github.com/ermongroup/csdi)
  * Adjusting for Autocorrelated Errors in Neural Networks for Time Series [[paper]](https://arxiv.org/abs/2101.12578) [[official code]](https://github.com/Daikon-Sun/AdjustAutocorrelation)
  * SSMF: Shifting Seasonal Matrix Factorization [[paper]](https://arxiv.org/abs/2110.12763) [[official code]](https://github.com/kokikwbt/ssmf)
  * Coresets for Time Series Clustering [[paper]](https://arxiv.org/abs/2110.15263)
  * Neural Flows: Efficient Alternative to Neural ODEs [[paper]](https://arxiv.org/abs/2110.13040) [[official code]](https://github.com/mbilos/neural-flows-experiments)
  * Spatio-Temporal Variational Gaussian Processes [[paper]](https://arxiv.org/pdf/2111.01732.pdf) [[official code]](https://github.com/aaltoml/spatio-temporal-gps)
  * Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers [[paper]](https://openreview.net/forum?id=A_Aeb-XLozL) [[official code]](https://github.com/SamsungLabs/Drop-DTW)

#### ICML 2021

##### Time Series Forecasting

  * Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting [[paper]](https://arxiv.org/abs/2101.12072) [[official code]](https://github.com/zalandoresearch/pytorch-ts)
  * End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series [[paper]](https://proceedings.mlr.press/v139/rangapuram21a.html) [[official code]](https://github.com/rshyamsundar/gluonts-hierarchical-ICML-2021)
  * RNN with particle flow for probabilistic spatio-temporal forecasting [[paper]](https://arxiv.org/abs/2106.06064) [[official code]](https://github.com/networkslab/rnn_flow)
  * Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting [[paper]](https://arxiv.org/abs/2105.04100) [[official code]](https://github.com/Z-GCNETs/Z-GCNETs)
  * Variance Reduction in Training Forecasting Models with Subgroup Sampling [[paper]](https://arxiv.org/abs/2103.02062)
  * Explaining Time Series Predictions With Dynamic Masks [[paper]](https://arxiv.org/abs/2106.05303) [[official code]](https://github.com/JonathanCrabbe/Dynamask)
  * Conformal prediction interval for dynamic time-series [[paper]](https://arxiv.org/abs/2010.09107) [[official code]](https://github.com/hamrel-cxu/EnbPI)

##### Time Series Anomaly Detection

  * Neural Transformation Learning for Deep Anomaly Detection Beyond Images [[paper]](https://arxiv.org/abs/2103.16440) [[official code]](https://github.com/boschresearch/NeuTraL-AD)
  * Event Outlier Detection in Continuous Time [[paper]](https://arxiv.org/abs/1912.09522) [[official code]](https://github.com/siqil/CPPOD)

##### Other Time Series Analysis

  * Voice2Series: Reprogramming Acoustic Models for Time Series Classification [[paper]](https://arxiv.org/abs/2106.09296) [[official code]](https://github.com/huckiyang/Voice2Series-Reprogramming)
  * Neural Rough Differential Equations for Long Time Series [[paper]](https://arxiv.org/abs/2009.08295) [[official code]](https://github.com/jambo6/neuralRDEs)
  * Neural Spatio-Temporal Point Processes [[paper]](https://arxiv.org/abs/2011.04583) [[official code]](https://github.com/facebookresearch/neural_stpp)
  * Learning Neural Event Functions for Ordinary Differential Equations [[paper]](https://arxiv.org/abs/2011.03902) [[official code]](https://github.com/rtqichen/torchdiffeq)
  * Approximation Theory of Convolutional Architectures for Time Series Modelling [[paper]](https://arxiv.org/abs/2107.09355)
  * Whittle Networks: A Deep Likelihood Model for Time Series [[paper]](https://proceedings.mlr.press/v139/yu21c.html) [[official code]](https://github.com/ml-research/WhittleNetworks)
  * Necessary and sufficient conditions for causal feature selection in time series with latent common causes [[paper]](http://proceedings.mlr.press/v139/mastakouri21a.html)

#### ICLR 2021

##### Time Series Forecasting

  * Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows [[paper]](https://openreview.net/forum?id=WiGQBFuVRv) [[official code]](https://github.com/zalandoresearch/pytorch-ts)
  * Discrete Graph Structure Learning for Forecasting Multiple Time Series [[paper]](https://openreview.net/forum?id=WEHSlH5mOk) [[official code]](https://github.com/chaoshangcs/GTS)

##### Other Time Series Analysis

  * Clairvoyance: A Pipeline Toolkit for Medical Time Series [[paper]](https://openreview.net/forum?id=xnC8YwKUE3k) [[official code]](https://github.com/vanderschaarlab/clairvoyance)
  * Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding [[paper]](https://openreview.net/forum?id=8qDwejCuCN) [[official code]](https://github.com/sanatonek/TNC_representation_learning)
  * Multi-Time Attention Networks for Irregularly Sampled Time Series [[paper]](https://openreview.net/forum?id=4c0J6lwQ4_) [[official code]](https://github.com/reml-lab/mTAN)
  * Generative Time-series Modeling with Fourier Flows [[paper]](https://openreview.net/forum?id=PpshD0AXfA) [[official code]](https://github.com/ahmedmalaa/Fourier-flows)
  * Differentiable Segmentation of Sequences [[paper]](https://openreview.net/forum?id=4T489T4yav) [[slides]](https://iclr.cc/media/Slides/iclr/2021/virtual%2805-08-00%29-05-08-00UTC-2993-differentiable_.pdf) [[official code]](https://github.com/diozaka/diffseg)
  * Neural ODE Processes [[paper]](https://openreview.net/forum?id=27acGyyI1BY) [[official code]](https://github.com/crisbodnar/ndp)
  * Learning Continuous-Time Dynamics by Stochastic Differential Networks [[paper]](https://openreview.net/forum?id=U850oxFSKmN) [[official code]]()

#### KDD 2021

##### Time Series Forecasting

  * ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting [[paper]](https://dl.acm.org/doi/10.1145/3447548.3467330) [[official code]](https://github.com/JLDeng/ST-Norm)
  * Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation [[paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467357)
  * Quantifying Uncertainty in Deep Spatiotemporal Forecasting [[paper]](https://arxiv.org/abs/2105.11982)
  * Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting [[paper]](https://arxiv.org/abs/2106.12931) [[official code]](https://github.com/square-coder/STGODE)
  * TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction [[paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467236)
  * Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting [[paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467275)

##### Time Series Anomaly Detection

  * Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding [[paper]](https://netman.aiops.org/wp-content/uploads/2021/08/KDD21_InterFusion_Li.pdf) [[official code]](https://github.com/zhhlee/InterFusion)
  * Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization [[paper]](https://dl.acm.org/doi/10.1145/3447548.3467174) [[official code]](https://github.com/eBay/RANSynCoders)
  * Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering [[paper]](https://arxiv.org/abs/2106.07992) [[official code]](https://arxiv.org/abs/2106.07992)
  * Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [[paper]](https://arxiv.org/abs/2008.13361) [[official code]](https://github.com/wzwtrevor/Multi-Scale-One-Class-Recurrent-Neural-Networks)

##### Other Time Series Analysis

  * Representation Learning of Multivariate Time Series using a Transformer Framework [[paper]](https://arxiv.org/abs/2010.02803) [[official code]](https://github.com/gzerveas/mvts_transformer)
  * Causal and Interpretable Rules for Time Series Analysis [[paper]](https://josselin-garnier.org/wp-content/uploads/2021/10/kdd21.pdf)
  * MiniRocket: A Fast (Almost) Deterministic Transform for Time Series Classification [[paper]](https://arxiv.org/abs/2012.08791) [[official code]](https://github.com/angus924/minirocket)
  * Statistical models coupling allows for complex localmultivariate time series analysis [[paper]](https://dl.acm.org/doi/abs/10.1145/3447548.3467362)
  * Fast and Accurate Partial Fourier Transform for Time Series Data [[paper]](https://jungijang.github.io/resources/2021/KDD/pft.pdf) [[official code]](https://github.com/snudatalab/PFT)
  * Deep Learning Embeddings for Data Series Similarity Search [[paper]](https://qtwang.github.io/assets/pdf/kdd21-seanet.pdf) [[link]](https://qtwang.github.io/kdd21-seanet)

#### AAAI 2021

##### Time Series Forecasting

  * Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [[paper]](https://arxiv.org/abs/2012.07436) [[official code]](https://github.com/zhouhaoyi/Informer2020)
  * Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting [[paper]](https://arxiv.org/abs/2009.05135) [[official code]](https://github.com/ostadabbas/DSARF)
  * Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series [[paper]](https://arxiv.org/abs/2103.02164) [[official code]](https://github.com/thuwuyinjun/DGM2)
  * Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting [[paper]](https://arxiv.org/abs/2101.10460)
  * Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [[paper]](https://arxiv.org/abs/2102.00431)
  * Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting [[paper]](https://arxiv.org/abs/2002.02887)
  * Attentive Neural Point Processes for Event Forecasting [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16929) [[official code]](https://github.com/guyulongcs/AAAI2021_ANPP)
  * Forecasting Reservoir Inflow via Recurrent Neural ODEs [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17763)
  * Hierarchical Graph Convolution Network for Traffic Forecasting [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16088)
  * Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [[paper]](https://arxiv.org/abs/2110.04038) [[official code]](https://github.com/jillbetty001/ST-GDN)
  * Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [[paper]](https://arxiv.org/abs/2012.09641) [[official code]](https://github.com/MengzhangLI/STFGNN)
  * FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting [[paper]](https://arxiv.org/abs/2007.15531) [[official code]](https://github.com/boreshkinai/fc-gaga)
  * Fairness in Forecasting and Learning Linear Dynamical Systems [[paper]](https://arxiv.org/abs/2006.07315)
  * A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16856)
  * Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16090)

##### Time Series Anomaly Detection

  * Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [[paper]](https://arxiv.org/abs/2106.06947) [[official code]](https://github.com/d-ailin/GDN)
  * Time Series Anomaly Detection with Multiresolution Ensemble Decoding [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17152)
  * Outlier Impact Characterization for Time Series Data [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17379)

##### Time Series Classification

  * Correlative Channel-Aware Fusion for Multi-View Time Series Classification [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/16830/16637)
  * Learnable Dynamic Temporal Pooling for Time Series Classification [[paper]](https://arxiv.org/abs/2104.02577) [[official code]](https://github.com/donalee/DTW-Pool)
  * ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17018)
  * Joint-Label Learning by Dual Augmentation for Time Series Classification [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17071)

##### Other Time Series Analysis

  * Time Series Domain Adaptation via Sparse Associative Structure Alignment [[paper]](https://arxiv.org/abs/2012.11797) [[official code]](https://github.com/DMIRLAB-Group/SASA)
  * Learning Representations for Incomplete Time Series Clustering [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17070)
  * Generative Semi-Supervised Learning for Multivariate Time Series Imputation [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17086) [[official code]](https://github.com/zjuwuyy-DL/Generative-Semi-supervised-Learning-for-Multivariate-Time-Series-Imputation)
  * Second Order Techniques for Learning Time-Series with Structural Breaks [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/17117)

#### IJCAI 2021

##### Time Series Forecasting

  * Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting [[paper]](https://www.ijcai.org/proceedings/2021/397)
  * Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks [[paper]](https://www.ijcai.org/proceedings/2021/374)
  * Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction [[paper]](https://www.ijcai.org/proceedings/2021/0508.pdf)
  * TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [[paper]](https://arxiv.org/abs/2106.06273) [[official code]](https://arxiv.org/abs/2106.06273)

##### Other Time Series Analysis

  * Time Series Data Augmentation for Deep Learning: A Survey [[paper]](https://arxiv.org/abs/2002.12478)
  * Time-Series Representation Learning via Temporal and Contextual Contrasting [[paper]](https://arxiv.org/abs/2106.14112) [[official code]](https://arxiv.org/abs/2106.14112)
  * Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation [[paper]](https://www.ijcai.org/proceedings/2021/378) [[official code]](https://github.com/jarheadjoe/Adv-spec-ker-matching)
  * Time-Aware Multi-Scale RNNs for Time Series Modeling [[paper]](https://www.ijcai.org/proceedings/2021/315)
  * TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [[paper]](https://arxiv.org/abs/2105.00412)

#### SIGMOD VLDB ICDE 2021

##### Time Series Forecasting

  * AutoAI-TS:AutoAI for Time Series Forecasting, SIGMOD’21. [[paper]](https://arxiv.org/abs/2102.12347)
  * FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data, VLDB’21. [[paper]](http://vldb.org/pvldb/vol14/p721-ding.pdf)
  * MDTP: a multi-source deep traffic prediction framework over spatio-temporal trajectory data, VLDB’21. [[paper]]()
  * EnhanceNet: Plugin Neural Networks for Enhancing Correlated Time Series Forecasting, ICDE’21. [[paper]](https://ieeexplore.ieee.org/document/9458855) [[slides]](https://pdfs.semanticscholar.org/3cb0/6f67fbfcf3c2dac32c02248a03eb84cc246d.pdf)
  * An Effective Joint Prediction Model for Travel Demands and Traffic Flows, ICDE’21. [[paper]](https://dbgroup.cs.tsinghua.edu.cn/ligl/papers/icde21-traffic.pdf)

##### Time Series Anomaly Detection

  * Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series, VLDB’21. [[paper]](https://arxiv.org/abs/2010.05073) [[official code]](https://github.com/exathlonbenchmark/exathlon)
  * SAND: Streaming Subsequence Anomaly Detection, VLDB’21. [[paper]](http://vldb.org/pvldb/vol14/p1717-boniol.pdf)

##### Other Time Series Analysis

  * RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection, SIGMOD’21. [[paper]](https://arxiv.org/abs/2002.09535) [

](https://github.com/ariaghora/robust-period)

ORBITS: Online Recovery of Missing Values in Multiple Time Series Streams, VLDB’21. [paper] [official code]
Missing Value Imputation on Multidimensional Time Series, VLDB’21. [paper]

Misc 2021

Time Series Forecasting

DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities, WWW’21. [paper] [official code]
AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph, WWW’21. [paper] [official code]
REST: Reciprocal Framework for Spatiotemporal-coupled Predictions, WWW’21. [paper]
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series, AISTATS’21. [paper]
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting, ICDM’21. [paper]
AdaRNN: Adaptive Learning and Forecasting of Time Series, CIKM’21. [paper] [official code]
Learning to Learn the Future: Modeling Concept Drifts in Time Series Prediction, CIKM’21. [paper]
Stock Trend Prediction with Multi-Granularity Data: A Contrastive Learning Approach with Adaptive Fusion, CIKM’21. [paper]
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction, CIKM’21. [paper] [official code1] [official code2]
Long Horizon Forecasting With Temporal Point Processes, WSDM’21. [paper] [official code]
Time-Series Event Prediction with Evolutionary State Graph, WSDM’21. [paper] [official code].

Time Series Anomaly Detection

SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs, WWW’21. [paper]
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding, WWW’21. [paper] [official code]
FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection, WSDM’21. [paper]
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping, ICCV’21. [paper] [official code]
Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems, ATC’21. [paper]

Other Time Series Analysis

Network of Tensor Time Series, WWW’21. [paper] [official code]
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series, WWW’21. [paper] [official code]
SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series, WWW’21. [paper]
Deep Fourier Kernel for Self-Attentive Point Processes, AISTATS’21. [paper]
Differentiable Divergences Between Time Series, AISTATS’21. [paper] [official code]
Aligning Time Series on Incomparable Spaces, AISTATS’21. [paper] [official code]
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions, ICDM’21. [paper]
Towards Generating Real-World Time Series Data, ICDM’21. [paper] [official code]
Learning Saliency Maps to Explain Deep Time Series Classifiers, CIKM’21. [paper] [official code]
Actionable Insights in Urban Multivariate Time-series, CIKM’21. [paper]
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals, WSDM’21. [paper]

AI4TS Papers 201X-2020 Selected

NeurIPS 201X-2020

Time Series Forecasting

Adversarial Sparse Transformer for Time Series Forecasting, NeurIPS’20. [paper] [official code]
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting, NeurIPS’20. [paper] [official code]
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting, NeurIPS’20. [paper]
Probabilistic Time Series Forecasting with Shape and Temporal Diversity, NeurIPS’20. [paper] [official code]
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting, NeurIPS’20. [paper] [official code]
Interpretable Sequence Learning for Covid-19 Forecasting, NeurIPS’20. [paper]
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting, NeurIPS’19. [paper] [

复制代码

](https://github.com/mlpotter/Transformer_Time_Series)
  * Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting, NeurIPS’19. [[paper]](https://arxiv.org/abs/1905.03806) [[official code]](https://github.com/rajatsen91/deepglo)
  * High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes, NeurIPS’19. [[paper]](https://arxiv.org/abs/1910.03002) [[official code]](https://github.com/mbohlkeschneider/gluon-ts)
  * Deep State Space Models for Time Series Forecasting, NeurIPS’18. [[paper]](https://proceedings.neurips.cc/paper/2018/hash/5cf68969fb67aa6082363a6d4e6468e2-Abstract.html)
  * Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction, NeurIPS’16. [[paper]](https://papers.nips.cc/paper/2016/hash/85422afb467e9456013a2a51d4dff702-Abstract.html)

##### Time Series Anomaly Detection

  * Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network, NeurIPS’20. [[paper]](https://proceedings.neurips.cc/paper/2020/hash/97e401a02082021fd24957f852e0e475-Abstract.html)
  * PIDForest: Anomaly Detection via Partial Identification, NeurIPS’19. [[paper]](https://arxiv.org/abs/1912.03582) [[official code]](https://github.com/vatsalsharan/pidforest)
  * Precision and Recall for Time Series, NeurIPS’18. [[paper]](https://arxiv.org/abs/1803.03639) [[official code]](https://github.com/IntelLabs/TSAD-Evaluator)

##### Time Series Classification

  * Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices, NeurIPS’19. [[paper]](https://proceedings.neurips.cc/paper/2019/hash/76d7c0780ceb8fbf964c102ebc16d75f-Abstract.html)

##### Time Series Clustering

  * Learning Representations for Time Series Clustering, NeurIPS’19. [[paper]](https://papers.nips.cc/paper/2019/hash/1359aa933b48b754a2f54adb688bfa77-Abstract.html) [[official code]](https://github.com/qianlima-lab/DTCR)
  * Learning low-dimensional state embeddings and metastable clusters from time series data, NeurIPS’19. [[paper]](https://arxiv.org/abs/1906.00302)

##### Time Series Imputation

  * NAOMI: Non-autoregressive multiresolution sequence imputation, NeurIPS’19. [[paper]](https://arxiv.org/abs/1901.10946) [[official code]](https://github.com/felixykliu/NAOMI)
  * BRITS: Bidirectional Recurrent Imputation for Time Series, NeurIPS’18. [[paper]](https://arxiv.org/abs/1805.10572) [[official code]](https://github.com/caow13/BRITS)
  * Multivariate Time Series Imputation with Generative Adversarial Networks, NeurIPS’18. [[paper]](https://papers.nips.cc/paper/2018/hash/96b9bff013acedfb1d140579e2fbeb63-Abstract.html) [[official code]](https://github.com/Luoyonghong/Multivariate-Time-Series-Imputation-with-Generative-Adversarial-Networks)

##### Time Series Neural xDE

  * Neural Controlled Differential Equations for Irregular Time Series, NeurIPS’20. [[paper]](https://arxiv.org/abs/2005.08926) [[official code]](https://github.com/patrick-kidger/NeuralCDE)
  * GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series, NeurIPS’19. [[paper]](https://arxiv.org/abs/1905.12374) [[official code]](https://github.com/edebrouwer/gru_ode_bayes)
  * Latent Ordinary Differential Equations for Irregularly-Sampled Time Series, NeurIPS’19. [[paper]](https://arxiv.org/abs/1907.03907) [[official code]](https://github.com/YuliaRubanova/latent_ode)
  * Neural Ordinary Differential Equations, NeurIPS’18. [[paper]](https://arxiv.org/abs/1806.07366) [[official code]](https://github.com/rtqichen/torchdiffeq)

##### General Time Series Analysis

  * High-recall causal discovery for autocorrelated time series with latent confounders, NeurIPS’20. [[paper]](https://proceedings.neurips.cc/paper/2020/hash/94e70705efae423efda1088614128d0b-Abstract.html) [[paper2]](https://arxiv.org/abs/2007.01884) [[official code]](https://github.com/jakobrunge/tigramite)
  * Benchmarking Deep Learning Interpretability in Time Series Predictions, NeurIPS’20. [[paper]](https://arxiv.org/abs/2010.13924) [[official code]](https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark)
  * What went wrong and when? Instance-wise feature importance for time-series black-box models, NeurIPS’20. [[paper]](https://arxiv.org/abs/2003.02821) [[official code]]()
  * Normalizing Kalman Filters for Multivariate Time Series Analysis, NeurIPS’20. [[paper]](https://proceedings.neurips.cc/paper/2020/hash/1f47cef5e38c952f94c5d61726027439-Abstract.html)
  * Unsupervised Scalable Representation Learning for Multivariate Time Series, NeurIPS’19. [[paper]](https://arxiv.org/abs/1901.10738) [[official code]](https://github.com/White-Link/UnsupervisedScalableRepresentationLearningTimeSeries)
  * Time-series Generative Adversarial Networks, NeurIPS’19. [[paper]](https://papers.nips.cc/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) [[official code]](https://github.com/jsyoon0823/TimeGAN)
  * U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging, NeurIPS’19. [[paper]](https://arxiv.org/abs/1910.11162) [[official code]](https://github.com/perslev/U-Time)
  * Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders, NeurIPS’18. [[paper]](https://arxiv.org/abs/1810.10107)
  * Safe Active Learning for Time-Series Modeling with Gaussian Processes, NeurIPS’18. [[paper]](https://proceedings.neurips.cc/paper/2018/hash/b197ffdef2ddc3308584dce7afa3661b-Abstract.html)

#### ICML 201X-2020

##### General Time Series Analysis

  * Learning from Irregularly-Sampled Time Series: A Missing Data Perspective, ICML’20. [[paper]](https://arxiv.org/abs/2008.07599) [[official code]](https://github.com/steveli/partial-encoder-decoder)
  * Set Functions for Time Series, ICML’20. [[paper]](https://arxiv.org/abs/1909.12064) [[official code]](https://github.com/BorgwardtLab/Set_Functions_for_Time_Series)
  * Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, ICML’20. [[paper]](https://arxiv.org/abs/1902.00450) [[official code]](https://github.com/ioanabica/Time-Series-Deconfounder)
  * Spectral Subsampling MCMC for Stationary Time Series, ICML’20. [[paper]](https://proceedings.mlr.press/v119/salomone20a.html)
  * Learnable Group Transform For Time-Series, ICML’20. [[paper]](https://proceedings.mlr.press/v119/cosentino20a.html)
  * Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models, ICML’19. [[paper]](https://arxiv.org/abs/1905.10857) [[official code]](https://github.com/Biwei-Huang/Causal-discovery-and-forecasting-in-nonstationary-environments)
  * Discovering Latent Covariance Structures for Multiple Time Series, ICML’19. [[paper]](https://arxiv.org/abs/1703.09528)
  * Autoregressive convolutional neural networks for asynchronous time series, ICML’18. [[paper]](https://arxiv.org/abs/1703.04122) [[official code]](https://github.com/mbinkowski/nntimeseries)
  * Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series, ICML’18. [[paper]](https://proceedings.mlr.press/v80/che18a.html)
  * Soft-DTW: a Differentiable Loss Function for Time-Series, ICML’17. [[paper]](https://arxiv.org/abs/1703.01541) [[official code]](https://github.com/mblondel/soft-dtw)

##### Time Series Forecasting

  * Forecasting Sequential Data Using Consistent Koopman Autoencoders, ICML’20. [[paper]](https://arxiv.org/abs/2003.02236) [[official code]](https://github.com/erichson/koopmanAE)
  * Adversarial Attacks on Probabilistic Autoregressive Forecasting Models, ICML’20. [[paper]](https://arxiv.org/abs/2003.03778) [[official code]](https://github.com/eth-sri/probabilistic-forecasts-attacks)
  * Influenza Forecasting Framework based on Gaussian Processes, ICML’20. [[paper]](http://proceedings.mlr.press/v119/zimmer20a.html)
  * Deep Factors for Forecasting, ICML’19. [[paper]](https://arxiv.org/abs/1905.12417)
  * Coherent Probabilistic Forecasts for Hierarchical Time Series, ICML’17. [[paper]](https://proceedings.mlr.press/v70/taieb17a.html)

#### ICLR 201X-2020

##### General Time Series Analysis

  * Interpolation-Prediction Networks for Irregularly Sampled Time Series, ICLR’19. [[paper]](https://openreview.net/forum?id=r1efr3C9Ym) [[official code]](https://github.com/mlds-lab/interp-net)
  * SOM-VAE: Interpretable Discrete Representation Learning on Time Series, ICLR’19. [[paper]](https://openreview.net/forum?id=rygjcsR9Y7) [[official code]](https://github.com/ratschlab/SOM-VAE)

##### Time Series Forecasting

  * N-BEATS: Neural basis expansion analysis for interpretable time series forecasting, ICLR’20. [[paper]](https://openreview.net/forum?id=r1ecqn4YwB) [[official code]](https://github.com/ElementAI/N-BEATS)
  * Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting, ICLR’18. [[paper]](https://openreview.net/forum?id=SJiHXGWAZ) [[official code]](https://github.com/liyaguang/DCRNN)
  * Automatically Inferring Data Quality for Spatiotemporal Forecasting, ICLR’18. [[paper]](https://openreview.net/forum?id=ByJIWUnpW)

#### KDD 201X-2020

##### General Time Series Analysis

  * Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns, KDD’20. [[paper]](https://www.researchgate.net/profile/Qingsong-Wen/publication/343780200_Fast_RobustSTL_Efficient_and_Robust_Seasonal-Trend_Decomposition_for_Time_Series_with_Complex_Patterns/links/614b9828a3df59440ba498b3/Fast-RobustSTL-Efficient-and-Robust-Seasonal-Trend-Decomposition-for-Time-Series-with-Complex-Patterns.pdf) [

](https://github.com/ariaghora/fast-robust-stl)

Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data, KDD’20. [paper] [official code]
Online Amnestic DTW to allow Real-Time Golden Batch Monitoring, KDD’19. [paper]
Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis, KDD’18. [paper]
Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data, KDD’17. [paper]

Time Series Forecasting

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks, KDD’20. [paper] [official code]
Attention based Multi-Modal New Product Sales Time-series Forecasting, KDD’20. [paper]
Forecasting the Evolution of Hydropower Generation, KDD’20. [paper]
Modeling Extreme Events in Time Series Prediction, KDD’19. [paper]
Multi-Horizon Time Series Forecasting with Temporal Attention Learning, KDD’19. [paper]
Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions, KDD’19. [paper]
Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units, KDD’19. [paper] [official code]
Dynamic Modeling and Forecasting of Time-evolving Data Streams, KDD’19. [paper] [official code]
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events, KDD’19. [paper] [official code]
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD’17. [paper] [official code]

Time Series Anomaly Detection

USAD: UnSupervised Anomaly Detection on Multivariate Time Series, KDD’20. [paper] [official code]
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks, KDD’20 MiLeTS. [paper]
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network, KDD’19. [paper] [official code]
Time-Series Anomaly Detection Service at Microsoft, KDD’19. [paper]
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding, KDD’18. [paper] [official code]
Anomaly Detection in Streams with Extreme Value Theory, KDD’17. [paper]

AAAI 201X-2020

General Time Series Analysis

Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets, AAAI’20. [paper] [official code]
DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series, AAAI’20. [paper]
Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series, AAAI’20. [paper] [official code]
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series, AAAI’20. [paper] [official code]
Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning, AAAI’20. [paper]
TapNet: Multivariate Time Series Classification with Attentional Prototype Network, AAAI’20. [paper] [official code]
RobustSTL: A Robust Seasonal-Trend Decomposition Procedure for Long Time Series, AAAI’19. [paper] [

复制代码

](https://github.com/LeeDoYup/RobustSTL)
  * Estimating the Causal Effect from Partially Observed Time Series, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/4281)
  * Adversarial Unsupervised Representation Learning for Activity Time-Series, AAAI’19. [[paper]](https://arxiv.org/abs/1811.06847)
  * Fourier Feature Approximations for Periodic Kernels in Time-Series Modelling, AAAI’18. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/11696)

##### Time Series Forecasting

  * Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values, AAAI’20. [[paper]](https://arxiv.org/abs/1911.10273)
  * Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting, AAAI’20. [[paper]](https://arxiv.org/abs/2002.12135) [[official code]](https://github.com/yokotatsuya/BHT-ARIMA)
  * Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting, AAAI’20. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/5438) [[official code]](https://github.com/Davidham3/STSGCN)
  * Self-Attention ConvLSTM for Spatiotemporal Prediction, AAAI’20. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/6819)
  * Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting, AAAI’20. [[paper]](https://arxiv.org/abs/1911.12093)
  * Spatio-Temporal Graph Structure Learning for Traffic Forecasting, AAAI’20. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/5470)
  * GMAN: A Graph Multi-Attention Network for Traffic Prediction, AAAI’20. [[paper]](https://arxiv.org/abs/1911.08415) [[official code]](https://github.com/zhengchuanpan/GMAN)
  * Cogra: Concept-drift-aware Stochastic Gradient Descent for Time-series Forecasting, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/4383)
  * Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/3877)
  * Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting, AAAI’19. [[paper]](https://ojs.aaai.org//index.php/AAAI/article/view/3881) [[official code]](https://github.com/guoshnBJTU/ASTGCN-r-pytorch)
  * MRes-RGNN: A Novel Deep Learning based Framework for Traffic Prediction, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/3821)
  * DeepSTN+: Context-aware Spatial Temporal Neural Network for Crowd Flow Prediction in Metropolis, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/3892) [[official code]](https://github.com/FIBLAB/DeepSTN)
  * Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting, AAAI’19. [[paper]](http://cs.emory.edu/~lzhao41/materials/papers/main_AAAI2019.pdf)
  * Learning Heterogeneous Spatial-Temporal Representation for Bike-sharing Demand Prediction, AAAI’19. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/3890)
  * Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting, AAAI’19. [[paper]](https://ojs.aaai.org//index.php/AAAI/article/view/4247)

##### Time Series Anomaly Detection

  * A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data, AAAI’19. [[paper]](https://arxiv.org/abs/1811.08055)
  * Non-parametric Outliers Detection in Multiple Time Series A Case Study: Power Grid Data Analysis, AAAI’18. [[paper]](https://ojs.aaai.org/index.php/AAAI/article/view/11632)

#### IJCAI 201X-2020

##### General Time Series Analysis

  * RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering, IJCAI’19. [[paper]](https://arxiv.org/abs/1906.03751)
  * E2GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation, IJCAI’19. [[paper]](https://www.ijcai.org/Proceedings/2019/0429.pdf)
  * Causal Inference in Time Series via Supervised Learning, IJCAI’18. [[paper]](https://www.ijcai.org/proceedings/2018/282)

##### Time Series Forecasting

  * PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction, IJCAI’20. [[paper]](https://www.ijcai.org/proceedings/2020/610) [[official code]](https://github.com/NingxuanFeng/PewLSTM)
  * LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks, IJCAI’20. [[paper]](https://www.ijcai.org/proceedings/2020/326)
  * Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction, IJCAI’20. [[paper]](https://www.ijcai.org/proceedings/2020/601)
  * Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting, IJCAI’19. [[paper]](https://arxiv.org/abs/2102.00397)
  * Explainable Deep Neural Networks for Multivariate Time Series Predictions, IJCAI’19. [[paper]](https://www.ijcai.org/proceedings/2019/932)
  * Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. [[paper]](https://www.ijcai.org/proceedings/2018/519)
  * Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. [[paper]](https://arxiv.org/abs/1709.04875) [[official code]](https://github.com/VeritasYin/STGCN_IJCAI-18)
  * LC-RNN: A Deep Learning Model for Traffic Speed Prediction. [[paper]](https://www.ijcai.org/proceedings/2018/482)
  * GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction, IJCAI’18. [[paper]](https://www.ijcai.org/proceedings/2018/476) [[official code]](https://github.com/yoshall/GeoMAN)
  * Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency, IJCAI’18. [[paper]](https://www.ijcai.org/proceedings/2018/487)
  * NeuCast: Seasonal Neural Forecast of Power Grid Time Series, IJCAI’18. [[paper]](https://www.ijcai.org/Proceedings/2018/460) [[official code]](https://github.com/chenpudigege/NeuCast)
  * A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction, IJCAI’17. [[paper]](https://arxiv.org/abs/1704.02971) [

](https://paperswithcode.com/paper/a-dual-stage-attention-based-recurrent-neural)

Hybrid Neural Networks for Learning the Trend in Time Series, IJCAI’17. [paper]

Time Series Anomaly Detection

BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series, IJCAI’19. [paper] [official code]
Outlier Detection for Time Series with Recurrent Autoencoder Ensembles, IJCAI’19. [paper] [official code]
Stochastic Online Anomaly Analysis for Streaming Time Series, IJCAI’17. [paper]

Time Series Clustering

Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest, IJCAI’19. [paper]
Similarity Preserving Representation Learning for Time Series Clustering, IJCAI’19. [paper]

Time Series Classification

A new attention mechanism to classify multivariate time series, IJCAI’20. [paper]

SIGMOD VLDB ICDE 201X-2020

General Time Series Analysis

Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures, SIGMOD’20. [paper] [official code]
Database Workload Capacity Planning using Time Series Analysis and Machine Learning, SIGMOD’20. [paper]
Mind the gap: an experimental evaluation of imputation of missing values techniques in time series, VLDB’20. [paper] [official code]
Active Model Selection for Positive Unlabeled Time Series Classification, ICDE’20. [paper] [official code]
ExplainIt! – A declarative root-cause analysis engine for time series data, SIGMOD’19. [paper]
Cleanits: A Data Cleaning System for Industrial Time Series, VLDB’19. [paper]
Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series, SIGMOD’18. [paper]
Effective Temporal Dependence Discovery in Time Series Data, VLDB’18. [paper]

Time Series Anomaly Detection

Series2Graph: graph-based subsequence anomaly detection for time series, VLDB’20. [paper] [official code]
Neighbor Profile: Bagging Nearest Neighbors for Unsupervised Time Series Mining, ICDE’20. [paper]
Automated Anomaly Detection in Large Sequences, ICDE’20. [paper] [official code]
User-driven error detection for time series with events, ICDE’20. [paper]

Misc 201X-2020

General Time Series Analysis

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks, WWW’19. [paper] [official code]
GP-VAE: Deep probabilistic time series imputation, AISTATS’20. [paper] [official code]
DYNOTEARS: Structure Learning from Time-Series Data, AISTATS’20. [paper]
Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer, CIKM’20. [paper]
Order-Preserving Metric Learning for Mining Multivariate Time Series, ICDM’20. [paper]
Learning Periods from Incomplete Multivariate Time Series, ICDM’20. [paper]
Foundations of Sequence-to-Sequence Modeling for Time Series, AISTATS’19. [paper]

Time Series Forecasting

Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting, WWW’20. [paper]
HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction, WWW’20. [paper] [official code]
Traffic Flow Prediction via Spatial Temporal Graph Neural Network, WWW’20. [paper]
Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems, WWW’20. [paper]
Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting, WWW’20. [paper]
Spatiotemporal Hypergraph Convolution Network for Stock Movement Forecasting, ICDM’20. [paper]
Probabilistic Forecasting with Spline Quantile Function RNNs, AISTATS’19. [paper]
DSANet: Dual self-attention network for multivariate time series forecasting, CIKM’19. [paper]
RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data, CIKM’18. [paper]
Forecasting Wavelet Transformed Time Series with Attentive Neural Networks, ICDM’18. [paper]
A Flexible Forecasting Framework for Hierarchical Time Series with Seasonal Patterns: A Case Study of Web Traffic, SIGIR’18. [paper]
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks, SIGIR’18. [paper] [official code]

Time Series Anomaly Detection

Multivariate Time-series Anomaly Detection via Graph Attention Network, ICDM’20. [paper] [

复制代码

](https://github.com/ML4ITS/mtad-gat-pytorch)
  * MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives, ICDM’20. [[paper]](https://www.cs.ucr.edu/~eamonn/MERLIN_Long_version_for_website.pdf) [[official code]](https://sites.google.com/view/merlin-find-anomalies/MERLIN)
  * Cross-dataset Time Series Anomaly Detection for Cloud Systems, ATC’19. [[paper]](https://www.usenix.org/conference/atc19/presentation/zhang-xu)
  * Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, WWW’18. [[paper]](https://arxiv.org/abs/1802.03903) [[official code]](https://github.com/NetManAIOps/donut)

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AI for Time Series (AI4TS) Papers, Tutorials, and Surveys

Main Recent Update Note

Table of Contents

AI4TS Tutorials and Surveys

AI4TS Tutorials

AI4TS Surveys

General Time Series Survey

Time Series Forecasting Survey

Time Series Anomaly Detection Survey

Time Series Classification Survey

AI4TS Papers 2024

NeurIPS 2024

ICML 2024

ICLR 2024

Time Series Forecasting

Others

KDD 2024

WWW 2024

Time Series Forecasting

Time Series Anomaly Detection

Others

AAAI 2024

Time Series Forecasting

Time Series Classification, Clustering, Anomaly Detection

Others

AI4TS Papers 2023

NeurIPS 2023

Time Series Forecasting

Time Series Anomaly Detection, Classification

Others

ICML 2023

Time Series Forecasting

Time Series Anomaly Detection, Classification, Imputation, and XAI

Other Time Series Analysis

ICLR 2023

Time Series Forecasting

Time Series Anomaly Detection and Classification

Other Time Series Analysis

KDD 2023

Time Series Anomaly Detection

Time Series Forecasting

Time Series Forecasting (Traffic)

Time Series Imputation

Others

AAAI 2023

Time Series Forecasting

Other Time Series Analysis

AI4TS Papers 2022

NeurIPS 2022

Time Series Forecasting

Other Time Series Analysis

ICML 2022

Time Series Forecasting

Time Series Anomaly Detection

Other Time Series Analysis

ICLR 2022

Time Series Forecasting

Time Series Anomaly Detection

Time Series Classification

Other Time Series Analysis

KDD 2022

Time Series Forecasting

Misc 2021

Time Series Forecasting

Time Series Anomaly Detection

Other Time Series Analysis

AI4TS Papers 201X-2020 Selected

NeurIPS 201X-2020

Time Series Forecasting

Time Series Forecasting

Time Series Anomaly Detection

AAAI 201X-2020

General Time Series Analysis

Time Series Anomaly Detection

Time Series Clustering

Time Series Classification

SIGMOD VLDB ICDE 201X-2020

General Time Series Analysis