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It includes 14 subdivision directions such as time series forecasting, modeling, alignment, analysis, anomaly detection, contrastive learning, metric learning, classification, and clustering.
The following is the detailed list.
1. Time series forecasting
[1]FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
[Code]https://github.com/MAZiqing/FEDformer
[Published] ICML 2022
[Field] Long-term series forecasting
[2]TACTiS: Transformer-Attentional Couples for Time Series
[Code] https://github.com/ServiceNow/tactis
[Published] ICML 2022
[Field] Time Series Forecasting
[3]Domain Adaptation for Time Series Forecasting via Attention Sharing
[Code] https://github.com/leejoonhun/daf
[Published] ICML 2022
[Field] DA-based time series forecasting
[4]Adaptive Conformal Predictions for Time Series
[code] https://github.com/mzaffran/adaptiveconformalpredictionstimeseries
[Published] ICML 2022
[Field] Electricity Price Time Series Forecast
[5]OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
[Code] https://github.com/OFA-Sys/OFA
[Published] ICML 2022
[Field] Multimodal pre-training based on Sequence-to-Sequence
[6]Reinforcement Learning based Dynamic Model Combination for Time Series Forecasting
[Code] hNone
[Published] AAAI 2022
[Field] Time Series Forecasting Based on Reinforcement Learning
[7]Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
[Code] https://github.com/ts-kim/RevIN
[Published] ICLR 2022
[Field] Time Series Forecasting
[8]Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
[Code] https://github.com/Graph-Machine-Learning-Group/grin
[Published] ICLR 2022
[Field] Time Series Forecasting
[9]Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
[Code] https://github.com/alipay/Pyraformer
[Published] ICLR 2022
[Field] Time Series Forecasting
[10]TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting
[Code]None
[Published] ICLR 2022
[Field] Time Series Forecasting
[11]DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
[Code] https://github.com/weifantt/depts
[Published] ICLR 2022
[Field] Time Series Forecasting
[12]PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
[Code] https://github.com/awslabs/gluon-ts
[Published] ICLR 2022
[Field] Time Series Forecasting
[13]Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future
[Code] https://github.com/AdityaLab/Back2Future
[Published] ICLR 2022
[Field] Time series forecasting/epidemic forecasting
[14]CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
[Code] https://github.com/adityalab/camul
[Published] WWW 2022
[Field] Time Series Forecasting
[15]PopNet: Real-Time Population-Level Disease Prediction with Data Latency
[Code] https://github.com/v1xerunt/popnet
[Published] WWW 2022
[Field] Time Series Forecasting
[16]Multi-Granularity Residual Learning with Confidence Estimation for Time Series Prediction
[Code] h
[Published] WWW 2022
[Field] Time Series Forecasting
[17]Allocating Stimulus Checks in Times of Crisis
[Code] https://github.com/papachristoumarios/financial-contagion
[Published] WWW 2022
[Field] Time Series Forecasting
[18]Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
[Code] https://github.com/ant-research/RGSL
[Published] IJCAI 2022
[Field] Time Series Forecasting
[1]Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting
[Code]None
[Published] IJCAI 2022
[Field] Multivariate Time Series Forecasting
[20]DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
[Code] https://github.com/galib19/deepextrema-ijcai22-
[Published] IJCAI 2022
[Field] Time Series Forecasting
2. Time series modeling
[1]Modeling Irregular Time Series with Continuous Recurrent Units
[Code] https://github.com/boschresearch/continuous-recurrent-units
[Published] ICML 2022
[Field] Time Series Modeling with Irregular Sampling
[2]Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
[Code] https://github.com/tung-nd/tnp-pytorch
[Published] ICML 2022
[Field] Sequence Modeling, Neural Processes, Uncertainty
[3]Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
[Code] https://github.com/samuelstanton/lambo
[Published] ICML 2022
[Field] Biological sequence data modeling, Bayesian optimization
[4]Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
[Code]https://github.com/linjing7/VR-Baseline
[Published] ICML 2022
[Field] Sequence-to-Sequence computer vision model
[5]Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
[Code] https://github.com/data-iitd/neuroseqret
[Published] AAAI 2022
[Field] Point Process Combined Time Event Sequence
3. Time series alignment
[1]Closed-Form Diffeomorphic Transformations for Time Series Alignment
[Code] https://github.com/imartinezl/difw
[Published] ICML 2022
[field] time series alignment
4. Time series analysis
[1]Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
[Code] https://github.com/durstewitzlab/mmplrnn
[Published] ICML 2022
[Field] Multimodal Time Series Analysis
[2]Learning of Cluster-based Feature Importance for Electronic Health Record Time-series
[Code]None
[Published] ICML 2022
[Field] Electronic Health Record (EHR) Data Analysis
[3]Proximal Exploration for Model-guided Protein Sequence Design
[Code]None
[Published] ICML 2022
[Field] Protein sequence analysis
[4]Biological Sequence Design with GFlowNets
[Code] https://github.com/mj10/bioseq-gfn-al
[Published] ICML 2022
[Field] Biological sequence analysis
[5]SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks
[Code]https://github.com/samxuxiang/SkexGen
[Published] ICML 2022
[Field] Computer Aided Design (CAD) Structural Sequence Analysis
[6]Graph-Guided Network for Irregularly Sampled Multivariate Time Series
[Code] https://github.com/mims-harvard/raindrop
[Published] ICLR 2022
[Field] Irregularly adopted multivariate time series analysis method
[7]Huber Additive Models for Non-stationary Time Series Analysis
[Code]https://github.com/xianruizhong/SpHAM
[Published] ICLR 2022
[Field] Non-stationary time series analysis
[8]Coherence-based Label Propagation over Time Series for Accelerated Active Learning
[Code]None
[Published] ICLR 2022
[Domain] Lack of labeled time series analysis
[9]Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
[Code] https://github.com/reml-lab/hetvae
[Published] ICLR 2022
[Field] Irregular Sampling Time Series Analysis Method
5. Unsupervised/self-supervised time series
[1]Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion
[Code]None
[Published] ICML 2022
[Field] Unsupervised/Self-Supervised Time Series
[1]Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models
[Code] https://apple.github.io/ml-style-equalization/
[Published] ICML 2022
[Field] Controllable sequence generation, unsupervised
6. Time series anomaly detection
[1]Deep Variational Graph Convolutional Recurrent Network for Multivariate Time Series Anomaly Detection
[Code]None
[Published] ICML 2022
[Field] Multivariate Time Series Anomaly Detection
[2]Towards a Rigorous Evaluation of Time-series Anomaly Detection
[Code] https://github.com/tuslkkk/tadpak
[Published] AAAI 2022
[Field] Time Series Anomaly Detection Review
[3]Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
[Code] https://github.com/enyandai/ganf
[Published] ICLR 2022
[Field] Time Series Anomaly Detection
[4]Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
[Code] https://github.com/thuml/Anomaly-Transformer
[Published] ICLR 2022
[Field] Time Series Anomaly Detection
[5]A Semi-Supervised VAE Based Active Anomaly Detection Framework in Multivariate Time Series for Online Systems
[Code]None
[Published] WWW 2022
[Field] Time Series Anomaly Detection
[6]GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning
[Code]None
[Published] IJCAI 2022
[Field] Time Series Anomaly Detection
[7]Neural Contextual Anomaly Detection for Time Series
[Code] https://github.com/Francois-Aubet/gluon-ts
[Published] IJCAI 2022
[Field] Time Series Anomaly Detection
7. Time series comparative learning
[1]Utilizing Expert Features for Contrastive Learning of Time-Series Representations
[Code] https://github.com/boschresearch/expclr
[Published] ICML 2022
[Field] Time Series Contrastive Learning
8. Time series causal analysis
[1]CITRIS: Causal Identifiability from Temporal Intervened Sequences
[Code] https://github.com/phlippe/citris
[Published] ICML 2022
[Field] Time Series Causal Analysis
[2]Causal Conceptions of Fairness and their Consequences
[Code]None
[Published] ICML 2022
[Field] Causality, Fair Decision Algorithms
9. Time series metric learning
[1]I-SEA: Importance Sampling and Expected Alignment-based Deep Distance Metric Learning for Time Series Analysis and Embedding
[Code] https://github.com/srambhatla/ISEA
[Published] AAAI 2022
[Domain] Time Series Metric Learning
10. Time series generation
[1]Conditional Loss and Deep Euler Scheme for Time Series Generation
[Code]None
[Published] AAAI 2022
[field] time series generation
11. Time series clustering
[1]Clustering Interval-Censored Time-Series for Disease Phenotyping
[Code]None
[Published] AAAI 2022
[Field] Time Series Clustering
12. Time series classification
[1]Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis
[Code] https://github.com/tahabelkhouja/robust-training-for-time-series
[Published] AAAI 2022
[field] time series training
[2]T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
[Code]None
[Published] ICLR 2022
[Field] Time Series Classification
[3]Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
[Code] https://github.com/Wensi-Tang/OS-CNN
[Published] ICLR 2022
[Field] Time Series Classification
[4]EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
[Code]None
[Published] WWW 2022
[Field] Time Series Classification
[5]A Reinforcement Learning-Informed Pattern Mining Framework for Multivariate Time Series Classification
[Code]None
[Published] IJCAI 2022
[Field] Time Series Classification
13. Time series representation learning
[1]TS2Vec: Towards Universal Representation of Time Series
[Code] https://github.com/yuezhihan/ts2vec
[Published] AAAI 2022
[Field] Time Series Representation Learning
[2]CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
[Code] https://github.com/salesforce/CoST
[Published] ICLR 2022
[Field] Time Series Representation Learning
14. Sequence and recommendation
[1]Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
[Code] https://github.com/Mingyue-Cheng/NASR
[Published] WWW 2022
[Field] Sequence and Recommendation
[2]Sequential Recommendation with Decomposed Item Feature Routing
[Code]None
[Published] WWW 2022
[Field] Sequence and Recommendation
[3]Sequential Recommendation via Stochastic Self-Attention
[Code] https://github.com/zfan20/stosa
[Published] WWW 2022
[Field] Sequence and Recommendation
[4]Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
[Code] https://github.com/zfan20/STOSA
[Published] WWW 2022
[Field] Sequence and Recommendation
[5]Intent Contrastive Learning for Sequential Recommendation
[Code] https://github.com/salesforce/iclrec
[Published] WWW 2022
[Field] Sequence and Recommendation
[6]Filter-enhanced MLP is All You Need for Sequential Recommendation
[Code] https://github.com/RUCAIBox/FMLP-Rec
[Published] WWW 2022
[Field] Sequence and Recommendation
[7]Efficient Online Learning to Rank for Sequential Music Recommendation
[Code]None
[Published] WWW 2022
[Field] Sequence and Recommendation