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
- Artificial Intelligence: AAAI, IJCAI
- Data Management: SIGMOD, VLDB, ICDE
- Misc (selected): WWW, 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 found 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.
- [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!
- 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]
- 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]
- 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 2020. [paper]
- Transformers in Time Series: A Survey, in arXiv 2022. [paper] [GitHub Repo]
- 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]
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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
- 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]
- Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts [paper] [official code]
- 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] [official code]
- 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] [official code]
- FOGS: First-Order Gradient Supervision with Learning-based Graph for Traffic Flow Forecasting
- Neural Contextual Anomaly Detection for Time Series [paper]
- GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning
- A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification [paper]
- T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification
- METRO: A Generic Graph Neural Network Framework for Multivariate Time Series Forecasting, VLDB'22. [paper] [official code]
- AutoCTS: Automated Correlated Time Series Forecasting, VLDB'22. [paper]
- Towards Spatio-Temporal Aware Traffic Time Series Forecasting, ICDE'22. [paper] [official code]
- Sintel: A Machine Learning Framework to Extract Insights from Signals, SIGMOD'22. [paper] [official code]
- TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection, VLDB'22. [paper] [official code]
- TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data, VLDB'22. [paper] [official code]
- Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles, VLDB'22. [paper]
- Robust and Explainable Autoencoders for Time Series Outlier Detection, ICDE'22. [paper]
- Anomaly Detection in Time Series with Robust Variational Quasi-Recurrent Autoencoders, ICDE'22.
- IPS: Instance Profile for Shapelet Discovery for Time Series Classification, ICDE'22. [paper]
- Towards Backdoor Attack on Deep Learning based Time Series Classification, ICDE'22. [paper]
- OnlineSTL: Scaling Time Series Decomposition by 100x, VLDB'22. [paper]
- Efficient temporal pattern mining in big time series using mutual information, VLDB'22. [paper]
- Learning Evolvable Time-series Shapelets, ICDE'22.
- CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting, WWW'22. [paper] [official code]
- Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction, WWW'22. [paper]
- RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph, WWW'22. [paper]
- Robust Probabilistic Time Series Forecasting, AISTATS'22. [paper] [official code]
- Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting, AISTATS'22. [paper]
- Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection, AISTATS'22. [paper] [official code]
- A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems, WWW'22. [paper]
- Decoupling Local and Global Representations of Time Series, AISTATS'22. [paper] [official code]
- LIMESegment: Meaningful, Realistic Time Series Explanations, AISTATS'22. [paper]
- Using time-series privileged information for provably efficient learning of prediction models, AISTATS'22. [paper] [official code]
- Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation, AISTATS'22. [paper] [official code]
- EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting, WWW'22. [paper]
- Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting [paper] [official code]
- MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data [paper]
- Conformal Time-Series Forecasting [paper] [official code]
- Probabilistic Forecasting: A Level-Set Approach [paper]
- Topological Attention for Time Series Forecasting [paper]
- When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [paper] [official code]
- Monash Time Series Forecasting Archive [paper] [official code]
- Revisiting Time Series Outlier Detection: Definitions and Benchmarks [paper] [official code]
- Online false discovery rate control for anomaly detection in time series [paper]
- Detecting Anomalous Event Sequences with Temporal Point Processes [paper]
- Probabilistic Transformer For Time Series Analysis [paper]
- Shifted Chunk Transformer for Spatio-Temporal Representational Learning [paper]
- Deep Explicit Duration Switching Models for Time Series [paper] [official code]
- Time-series Generation by Contrastive Imitation [paper]
- CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [paper] [official code]
- Adjusting for Autocorrelated Errors in Neural Networks for Time Series [paper] [official code]
- SSMF: Shifting Seasonal Matrix Factorization [paper] [official code]
- Coresets for Time Series Clustering [paper]
- Neural Flows: Efficient Alternative to Neural ODEs [paper] [official code]
- Spatio-Temporal Variational Gaussian Processes [paper] [official code]
- Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers [paper] [official code]
- Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting [paper] [official code]
- End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series [paper] [official code]
- RNN with particle flow for probabilistic spatio-temporal forecasting [paper] [official code]
- Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting [paper] [official code]
- Variance Reduction in Training Forecasting Models with Subgroup Sampling [paper]
- Explaining Time Series Predictions With Dynamic Masks [paper] [official code]
- Conformal prediction interval for dynamic time-series [paper] [official code]
- Neural Transformation Learning for Deep Anomaly Detection Beyond Images [paper] [official code]
- Event Outlier Detection in Continuous Time [paper] [official code]
- Voice2Series: Reprogramming Acoustic Models for Time Series Classification [paper] [official code]
- Neural Rough Differential Equations for Long Time Series [paper] [official code]
- Neural Spatio-Temporal Point Processes [paper] [official code]
- Learning Neural Event Functions for Ordinary Differential Equations [paper] [official code]
- Approximation Theory of Convolutional Architectures for Time Series Modelling [paper]
- Whittle Networks: A Deep Likelihood Model for Time Series [paper] [official code]
- Necessary and sufficient conditions for causal feature selection in time series with latent common causes [paper]
- Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows [paper] [official code]
- Discrete Graph Structure Learning for Forecasting Multiple Time Series [paper] [official code]
- Clairvoyance: A Pipeline Toolkit for Medical Time Series [paper] [official code]
- Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding [paper] [official code]
- Multi-Time Attention Networks for Irregularly Sampled Time Series [paper] [official code]
- Generative Time-series Modeling with Fourier Flows [paper] [official code]
- Differentiable Segmentation of Sequences [paper] [slides] [official code]
- Neural ODE Processes [paper] [official code]
- Learning Continuous-Time Dynamics by Stochastic Differential Networks [paper] [official code]
- ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting [paper] [official code]
- Graph Deep Factors for Forecasting with Applications to Cloud Resource Allocation [paper]
- Quantifying Uncertainty in Deep Spatiotemporal Forecasting [paper]
- Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting [paper] [official code]
- TrajNet: A Trajectory-Based Deep Learning Model for Traffic Prediction [paper]
- Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting [paper]
- Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding [paper] [official code]
- Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization [paper] [official code]
- Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering [paper] [official code]
- Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection [paper] [official code]
- Representation Learning of Multivariate Time Series using a Transformer Framework [paper] [official code]
- Causal and Interpretable Rules for Time Series Analysis [paper]
- MiniRocket: A Fast (Almost) Deterministic Transform for Time Series Classification [paper] [official code]
- Statistical models coupling allows for complex localmultivariate time series analysis [paper]
- Fast and Accurate Partial Fourier Transform for Time Series Data [paper] [official code]
- Deep Learning Embeddings for Data Series Similarity Search [paper] [link]
- Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [paper] [official code]
- Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting [paper] [official code]
- Dynamic Gaussian Mixture Based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series [paper] [official code]
- Temporal Latent Autoencoder: A Method for Probabilistic Multivariate Time Series Forecasting [paper]
- Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [paper]
- Meta-Learning Framework with Applications to Zero-Shot Time-Series Forecasting [paper]
- Attentive Neural Point Processes for Event Forecasting [paper] [official code]
- Forecasting Reservoir Inflow via Recurrent Neural ODEs [paper]
- Hierarchical Graph Convolution Network for Traffic Forecasting [paper]
- Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network [paper] [official code]
- Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting [paper] [official code]
- FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting [paper] [official code]
- Fairness in Forecasting and Learning Linear Dynamical Systems [paper]
- A Multi-Step-Ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting [paper]
- Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances [paper]
- Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [paper] [official code]
- Time Series Anomaly Detection with Multiresolution Ensemble Decoding [paper]
- Outlier Impact Characterization for Time Series Data [paper]
- Correlative Channel-Aware Fusion for Multi-View Time Series Classification [paper]
- Learnable Dynamic Temporal Pooling for Time Series Classification [paper] [official code]
- ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification [paper]
- Joint-Label Learning by Dual Augmentation for Time Series Classification [paper]
- Time Series Domain Adaptation via Sparse Associative Structure Alignment [paper] [official code]
- Learning Representations for Incomplete Time Series Clustering [paper]
- Generative Semi-Supervised Learning for Multivariate Time Series Imputation [paper] [official code]
- Second Order Techniques for Learning Time-Series with Structural Breaks [paper]
- Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting [paper]
- Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks [paper]
- Hierarchical Adaptive Temporal-Relational Modeling for Stock Trend Prediction [paper]
- TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [paper] [official code]
- Time Series Data Augmentation for Deep Learning: A Survey [paper]
- Time-Series Representation Learning via Temporal and Contextual Contrasting [paper] [official code]
- Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation [paper] [official code]
- Time-Aware Multi-Scale RNNs for Time Series Modeling [paper]
- TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data [paper]
- AutoAI-TS:AutoAI for Time Series Forecasting, SIGMOD'21. [paper]
- FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data, VLDB'21. [paper]
- 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] [slides]
- An Effective Joint Prediction Model for Travel Demands and Traffic Flows, ICDE'21. [paper]
- Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series, VLDB'21. [paper] [official code]
- SAND: Streaming Subsequence Anomaly Detection, VLDB'21. [paper]
- RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection, SIGMOD'21. [paper] [code]
- 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]
- 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].
- 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]
- 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]
- 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] [code]
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- Stock Price Prediction via Discovering Multi-Frequency Trading Patterns, KDD'17. [paper] [official code]
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- Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting, AAAI'20. [paper] [official code]
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- MRes-RGNN: A Novel Deep Learning based Framework for Traffic Prediction, AAAI'19. [paper]
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- Database Workload Capacity Planning using Time Series Analysis and Machine Learning, SIGMOD'20. [paper]
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- Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, WWW'18. [paper] [official code]