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.
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.
- [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!
- 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]
- 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]
- 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]
- 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]
- 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]
- 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]
- Pathformer: 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]
- 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]
- 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]
- 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]
- Dynamic Multi-Network Mining of Tensor Time Series [paper]
- E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series [paper]
- 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]
- 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]
- 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]
- 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]
- 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]
- 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
- 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]
- 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]
- 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]
- 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]
- Unsupervised Model Selection for Time Series Anomaly Detection [paper] [official code]
- Out-of-distribution Representation Learning for Time Series Classification [paper] [official code]
- 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]
- 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]
- 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
- 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
- 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
- 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
- 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]
- 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]
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FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting [paper] [official code]
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SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction [paper] [official code]
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Non-stationary Transformers: Rethinking the Stationarity in Time Series Forecasting [paper]
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Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [paper]
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Generative Time Series Forecasting with Diffusion, Denoise and Disentanglement
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Learning Latent Seasonal-Trend Representations for Time Series Forecasting
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WaveBound: Dynamically Bounding Error for Stable Time Series Forecasting
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Time Dimension Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting
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Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks
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C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting
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Meta-Learning Dynamics Forecasting Using Task Inference [paper]
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Conformal Prediction with Temporal Quantile Adjustments
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Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency, [paper] [official code]
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Causal Disentanglement for Time Series
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BILCO: An Efficient Algorithm for Joint Alignment of Time Series
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Dynamic Sparse Network for Time Series Classification: Learning What to “See”
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AutoST: Towards the Universal Modeling of Spatio-temporal Sequences
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GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
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Efficient learning of nonlinear prediction models with time-series privileged information
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Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
- 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]
- Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection [paper]
- 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]
- 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]
- 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]
- 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]
- 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]
- Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting [code]
- 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
- Local Evaluation of Time Series Anomaly Detection Algorithms
- Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams
- 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
- CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting [paper]
- Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting [paper]
- DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation [paper] official code]
- PrEF: Probabilistic Electricity Forecasting via Copula-Augmented State Space Model [paper]
- LIMREF: Local Interpretable Model Agnostic Rule-Based Explanations for Forecasting, with an Application to Electricity Smart Meter Data [paper]
- Learning and Dynamical Models for Sub-Seasonal Climate Forecasting: Comparison and Collaboration [paper] [official code]
- CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting [paper]
- Conditional Local Convolution for Spatio-Temporal Meteorological Forecasting [paper] [official code]
- Graph Neural Controlled Differential Equations for Traffic Forecasting [paper] [official code]
- STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction [paper] [official code]
- Towards a Rigorous Evaluation of Time-Series Anomaly Detection [paper]
- AnomalyKiTS-Anomaly Detection Toolkit for Time Series [Demo paper]
- TS2Vec: Towards Universal Representation of Time Series [paper] [official code]
- I-SEA: Importance Sampling and Expected Alignment-Based Deep Distance Metric Learning for Time Series Analysis and Embedding [paper]
- Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis [paper]
- Conditional Loss and Deep Euler Scheme for Time Series Generation [paper]
- Clustering Interval-Censored Time-Series for Disease Phenotyping [paper]
- Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting [paper]
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- 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]
- 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]
- 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]
- 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]
- Multivariate Time-series Anomaly Detection via Graph Attention Network, ICDM'20. [paper] [code]
- MERLIN: Parameter-Free Discovery of Arbitrary Length Anomalies in Massive Time Series Archives, ICDM'20. [paper] [official code]
- Cross-dataset Time Series Anomaly Detection for Cloud Systems, ATC'19. [paper]
- Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, WWW'18. [paper] [official code]