Awesome: Irregular Time Series, Incomplete Time Series

• Definition: Irregular Time Series and Incomplete Time Series
• Related Surveys and Benchmark
• Paper List
  • Year2025
  • Year2024
  • Year2023
  • Year2022
  • Year2021
  • Year2020
  • Year2019
  • Others

What are Irregular Time Series and Incomplete Time Series?

An irregular time series can be represented as

where , is the number of samples, is the number of variables, is the length of observations. For each variable, the time point list of observations is irregular.

An incomplete time series can be represented as where denotes the incomplete values of $C$ features at the timestamp $t$, is the mask matrix. $m_t^i=1$ indicates $x_t^i$ is observed, and $m_t^i=0$ indicates $x_t^i$ is missing.

Irregular time series vs incomplete time series: Irregular time series usually refers to the irregular intervals between observation time points, while incomplete time series usually refers to the presence of missing values in the observed regular time series.

• : irregular time series: observation/sampling timestamps are irregular.
• : incomplete time series: regular time series with missing values.

Task: , , , , , , ,

Related Surveys & Benchmark

Year	Venue	Title	Type	Link
2024	Arxiv	An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data		[paper] [code]
2024	Arxiv	TSI-Bench: Benchmarking Time Series Imputation		[paper] [code]
2024	Arxiv	ITI-IQA: a Toolbox for Heterogeneous Univariate and Multivariate Missing Data Imputation Quality Assessment		[paper]
2024	Arxiv	Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset		[paper]
2024	Arxiv	Deep Learning for Multivariate Time Series Imputation: A Survey		[paper] [code]
2024	Arxiv	How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation		[paper]
2023	Arxiv	Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking		[paper]
2023	TKDE	An Experimental Survey of Missing Data Imputation Algorithms		[paper]
2023	IEEE Access	Generative Adversarial Networks Assist Missing Data Imputation: A Comprehensive Survey and Evaluation		[paper]
2022	ACM Computing Surveys	A Comprehensive Survey on Imputation of Missing Data in Internet of Things		[paper]
2022	Research in Social and Administrative Pharmacy	Missing data in surveys: Key concepts, approaches, and applications		[paper]
2020	VLDB	Mind the gap: an experimental evaluation of imputation of missing values techniques in time series		[paper] [code]
2020	NeurIPS Workshop	A Survey on Principles, Models and Methods for Learning from Irregularly Sampled Time Series		[paper]
2020	Arxiv	Time series data imputation: A survey on deep learning approaches		[paper]

Paper List

Year 2025

Venue	Title	Type	Task	Link
ICLR	Optimal Transport for Time Series Imputation			[paper] [code]
ICLR	DiffPuter: An EM-Driven Diffusion Model for Missing Data Imputation			[paper]
ICLR	Neural Wave Equation for Irregularly Sampled Sequence Data			[paper]
ICLR	Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series			[paper] [code]
ICLR	Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs			[paper] [code]
AAAI	Integrating Sequence and Image Modeling in Irregular Medical Time Series through Self-Supervised Learning			[paper] [code]
AAAI	Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios			[paper]
AAAI	Motif-aware Graph Neural Networks for Networked Time Series Imputation			[paper]
AAAI	Probabilistic Forecasting of Irregularly Sampled Time Series with Missing Values via Conditional Normalizing Flows			[paper] [code]
AAAI	RDPI: A Refine Diffusion Probability Generation Method for Spatiotemporal Data Imputation			[paper] [code]
AAAI	DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis			[paper] [code]
AAAI	TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis			[paper]
AAAI	Graph Structure Learning for Spatial-Temporal Imputation: Adapting to Node and Feature Scales			[paper] [code]
AAAI	KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy			[paper] [code]
Arxiv	Collaborative Imputation of Urban Time Series through Cross-city Meta-learning			[paper]
Arxiv	Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study			[paper]
Arxiv	CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation			[paper]
Arxiv	LSCD: Lomb–Scargle Conditioned Diffusion for Irregular Time series Imputation			[paper]

Year 2024

Venue	Title	Type	Task	Link
NeurIPS	SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Prediction			[paper] [code]
NeurIPS	SAND: Smooth Imputation Of Sparse And Noisy Functional Data With Transformer Networks			[paper] [code]
NeurIPS	Rethinking the Diffusion Models for Missing Data Imputation: A Gradient Flow Perspective			[paper] [code]
NeurIPS	Continuous Temporal Domain Generalization			[paper] [code]
NeurIPS	Unsupervised Anomaly Detection in The Presence of Missing Values			[paper] [code]
NeurIPS	Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series			[paper] [code]
NeurIPS	Learning from Highly Sparse Spatio-temporal Data			[paper] [code]
NeurIPS	Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series			[paper] [code]
NeurIPS	Frequency-aware Generative Models for Multivariate Time-series Imputation			[paper] [code]
NeurIPS	Task-oriented Time Series Imputation Evaluation via Generalized Representers			[paper] [code]
NeurIPS	Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking			[paper] [code]
NeurIPS Workshop	Efficient Modeling of Irregular Time-Series with Stochastic Optimal Control			[paper]
IJCAI	Temporal Graph ODEs for Irregularly-Sampled Time Series			[paper] [code]
IJCAI	Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation			[paper]
IJCAI	SaSDim:Self-adaptive Noise Scaling Diffusion Model for Spatial Time Series Imputation			[paper]
IJCAI	Reconstructing Missing Variables for Multivariate Time Series Forecasting via Conditional Generative Flows			[paper]
KDD	Mining of Switching Sparse Networks for Missing Value Imputation in Multivariate Time Series			[paper] [code]
KDD	ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions			[paper] [code]
KDD	Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask			[paper] [code]
KDD	Long-Term Vessel Trajectory Imputation with Physics-Guided Diffusion Probabilistic Model			[paper]
KDD	GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing			[paper] [code]
KDD	Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network			[paper] [code]
KDD	ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation			[paper] [code]
KDD	Fast and Accurate Domain Adaptation for Irregular Tensor Decomposition			[paper] [code]
KDD	Compact Decomposition of Irregular Tensors for Data Compression: From Sparse to Dense to High-Order Tensors			[paper] [code]
ICML	BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition			[paper] [code]
ICML	Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach			[paper] [code]
ICML	Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling			[paper] [code]
ICLR	Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data			[paper] [code]
ICLR	Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs			[paper] [code]
ICLR	Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values			[paper] [code]
ICLR	Generative Learning for Financial Time Series with Irregular and Scale-Invariant Patterns			[paper] [code]
ICLR	Conditional Information Bottleneck Approach for Time Series Imputation			[paper] [code]
AAAI	CUTS+: High-Dimensional Causal Discovery from Irregular Time-Series			[paper] [code]
AAAI	GraFITi: Graphs for Forecasting Irregularly Sampled Time Series			[paper] [code]
CVPR Workshop	Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series			[paper] [code]
ICDM	EMIT - Event-Based Masked Auto Encoding for Irregular Time Series			[paper] [code]
ICDM	GADIN: Generative Adversarial Denoise Imputation Network for Incomplete Data			[paper]
CIKM	Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics			[paper] [code]
CIKM	CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation			[paper]
CIKM	Periormer: Periodic Transformer for Seasonal and Irregularly Sampled Time Series			[paper] [code]
CIKM	A Spatio-Temporal Diffusion Model for Missing and Real-Time Financial Data Inference			[paper]
CIKM	MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation			[paper] [code]
CIKM	Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation			[paper] [code]
BIBM	Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation			[paper] [code]
WSDM	Continuous-time Autoencoders for Regular and Irregular Time Series Imputation			[paper]
ECML-PKDD	Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting			[paper]
AISTATS	Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning			[paper] [code]
AISTATS	SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data			[paper]
TKDE	Laplacian Convolutional Representation for Traffic Time Series Imputation			[paper]
TITS	FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-Temporal Traffic Data Imputation			[paper]
TITS	Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data			[paper]
TITS	Interpretable Traffic Accident Prediction: Attention Spatial–Temporal Multi-Graph Traffic Stream Learning Approach			[paper]
TKDD	Iterative Time Series Imputation by Maintaining Dependency Consistency			[paper] [code]
Information Fusion	Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values			[paper]
Pattern Recognition	Time pattern reconstruction for classification of irregularly sampled time series			[paper]
TSAE	An Efficient Dynamic Auto-Regressive CCA for Time Series Imputation With Irregular Sampling			[paper]
Information Processing & Management	Time-Enhanced Neighbor-Aware network on irregular time series for sentiment prediction in social networks			[paper]
Arxiv	Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation			[paper]
Arxiv	DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone			[paper]
Arxiv	Conditional Lagrangian Wasserstein Flow for Time Series Imputation			[paper]
Arxiv	From Incomplete Coarse-Grained to Complete Fine-Grained: A Two-Stage Framework for Spatiotemporal Data Reconstruction			[paper]
Arxiv	Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting			[paper]
Arxiv	TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis			[paper]
Arxiv	Latent Space Score-based Diffusion Model for Probabilistic Multivariate Time Series Imputation			[paper] [code]
Arxiv	An End-to-End Model for Time Series Classification In the Presence of Missing Values			[paper]
Arxiv	Unified Principal Components Analysis of Irregularly Observed Functional Time Series			[paper]
Arxiv	Unleash The Power of Pre-Trained Language Models for Irregularly Sampled Time Series			[paper]
Arxiv	Time Series Imputation with Multivariate Radial Basis Function Neural Network			[paper]
Arxiv	Generative Adversarial Networks for Imputing Sparse Learning Performance			[paper]
Arxiv	MUSE-Net: Missingness-aware mUlti-branching Self-attention Encoder for Irregular Longitudinal Electronic Health Records			[paper]
Arxiv	NuwaTS: a Foundation Model Mending Every Incomplete Time Series			[paper] [code]
Arxiv	Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning			[paper]
Arxiv	MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data			[paper]
Arxiv	Marginalization Consistent Mixture of Separable Flows for Probabilistic Irregular Time Series Forecasting			[paper]
Arxiv	Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series			[paper] [code]
Arxiv	VISTA-SSM: Varying and Irregular Sampling Time-series Analysis via State Space Models			[paper] [code]
Arxiv	Irregularly Sampled Time Series Interpolation for Detailed Binary Evolution Simulations			[paper]
Arxiv	Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data			[paper] [code]
Arxiv	Enhancing Glucose Level Prediction of ICU Patients through Irregular Time-Series Analysis and Integrated Representation			[paper] [code]
Arxiv	FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities			[paper] [code]
Arxiv	WaveGNN: Modeling Irregular Multivariate Time Series for Accurate Predictions			[paper]
Arxiv	Is Precise Recovery Necessary? A Task-Oriented Imputation Approach for Time Series Forecasting on Variable Subset			[paper]
Arxiv	Temporal Wasserstein Imputation: Versatile Missing Data Imputation for Time Series			[paper]
Arxiv	Federated Time Series Generation on Feature and Temporally Misaligned Data			[paper]

Year 2023

Venue	Title	Type	Task	Link
NeurIPS	ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling			[paper] [code]
NeurIPS	Time Series as Images: Vision Transformer for Irregularly Sampled Time Series			[paper] [code]
NeurIPS	Sparse Deep Learning for Time Series Data: Theory and Applications			[paper]
WWW	INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging			[paper] [code]
KDD	Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series			[paper] [code]
KDD	Precursor-of-Anomaly Detection for Irregular Time Series			[paper] [code]
KDD	Graph Neural Processes for Spatio-Temporal Extrapolation			[paper] [code]
KDD	Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders			[paper]
KDD	An Observed Value Consistent Diffusion Model for Imputing Missing Values in Multivariate Time Series			[paper]
KDD	The Missing Indicator Method: From Low to High Dimensions			[paper] [code]
KDD	Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models			[paper] [code]
ICLR	Multivariate time-series imputation with disentangled temporal representations			[paper] [code]
ICLR	CUTS: Neural Causal Discovery from Irregular Time-Series Data			[paper] [code]
ICML	Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series			[paper] [code]
ICML	Probabilistic Imputation for Time-series Classification with Missing Data			[paper] [code]
ICML	Regression with Sensor Data Containing Incomplete Observations			[paper]
ICML	Provably Convergent Schrodinger Bridge with Applications to Probabilistic Time Series Imputation			[paper] [code]
ICML	Deep Latent State Space Models for Time-Series Generation			[paper] [code]
AAAI	PrimeNet: Pre-training for Irregular Multivariate Time Series			[paper] [code]
AAAI	Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders			[paper] [code]
AAAI	Learnable Path in Neural Controlled Differential Equations			[paper] [code]
IJCAI	Prediction with Incomplete Data under Agnostic Mask Distribution Shift			[paper]
IJCAI	Incomplete Multi-view Clustering via Prototype-based Imputation			[paper] [code]
CIKM	TriD-MAE: A Generic Pre-trained Model for Multivariate Time Series with Missing Values			[paper]
CIKM	Density-Aware Temporal Attentive Step-wise Diffusion Model For Medical Time Series Imputation			[paper]
ICDM	Compatible Transformer for Irregularly Sampled Multivariate Time Series			[paper] [code]
ICDM	Uncertainty-aware Traffic Prediction under Missing Data			[paper] [code]
DASFAA	Adversarial Spatial-Temporal Graph Network for Traffic Speed Prediction with Missing Values			[paper]
ICASSP	NRTSI: Non-Recurrent Time Series Imputation for Irregularly-sampled Data			[paper] [code]
AISTATS	Temporal Graph Neural Networks for Irregular Data			[paper] [code]
AISTATS	Positional Encoder Graph Neural Networks for Geographic Data			[paper] [code]
AISTATS	To Impute or not to Impute? Missing Data in Treatment Effect Estimation			[paper] [code]
ITSC	ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks			[paper]
TNNLS	Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference			[paper] [code]
TON	Graph-Tensor Neural Networks for Network Traffic Data Imputation			[paper]
TITS	Traffic Prediction With Missing Data: A Multi-Task Learning Approach			[paper]
TETC	MissII: Missing Information Imputation for Traffic Data			[paper]
DMKD	Graph Convolutional Networks for Traffic Forecasting with Missing Values			[paper] [code]
MLHC	DuETT: Dual Event Time Transformer for Electronic Health Records			[paper] [code]
ESWA	SAITS: Self-Attention-based Imputation for Time Series			[paper] [code]
Arxiv	Knowledge Enhanced Conditional Imputation for Healthcare Time-series			[paper] [code]
Arxiv	No Imputation Needed: A Switch Approach to Irregularly Sampled Time Series			[paper]
Arxiv	An End-to-End Time Series Model for Simultaneous Imputation and Forecast			[paper]
Arxiv	Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding			[paper]
Arxiv	Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation			[paper]
Arxiv	PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Series			[paper] [code]

Year 2022

Venue	Title	Type	Task	Link
ICML	Modeling Irregular Time Series with Continuous Recurrent Units			[paper]
ICML	TACTiS: Transformer-Attentional Copulas for Time Series			[paper] [code]
ICML	Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling			[paper] [code]
ICLR	LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations			[paper] [code]
ICLR	Graph-Guided Network for Irregularly Sampled Multivariate Time Series			[paper] [code]
ICLR	Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks			[paper] [code]
ICLR	SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data			[paper] [code]
ICLR	Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series			[paper] [code]
NeurIPS	Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations			[paper] [code]
NeurIPS Workshop	Mixed-Memory RNNs for Learning Long-term Dependencies in Irregularly Sampled Time Series			[paper] [code]
TKDD	Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series			[paper] [code]
CIKM	Stop&Hop: Early Classification of Irregular Time Serie			[paper] [code]
ICASSP	Bayesian Continual Imputation and Prediction For Irregularly Sampled Time Series Data			[paper]
IEEE BigData	Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Series			[paper] [code]
TMLR	Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models			[paper] [code]

Year 2021

Venue	Title	Type	Task	Link
NeurIPS	CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation.			[paper] [code]
NeurIPS	Neural Flows: Efficient Alternative to Neural ODEs			[paper] [code]
NeurIPS Workshop	As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning			[paper] [code]
ICLR	Multi-Time Attention Networks for Irregularly Sampled Time Series			[paper] [code]
IJCAI	TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Data			[paper] [code]
IJCAI	Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data			[paper]
AAAI	Inductive Graph Neural Networks for Spatiotemporal Kriging			[paper] [code]
AAAI	Learning Representations for Incomplete Time Series Clustering			[paper] [code]
AAAI	Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series			[paper] [code]
AAAI	Generative semi-supervised learning for multivariate time series imputation			[paper] [code]
VLDB	Missing value imputation on multidimensional time series			[paper]
CIKM	BiCMTS: Bidirectional Coupled Multivariate Learning of Irregular Time Series with Missing Values			[paper]
CIKM	Improving Irregularly Sampled Time Series Learning with Time-Aware Dual-Attention Memory-Augmented Networks			[paper]
ICDM	MERITS: Medication Recommendation for Chronic Disease with Irregular Time-Series			[paper] [code]
ICDM	PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series			[paper]
ICDM	STING: Self-attention based Time-series Imputation Networks using GAN			[paper]
ICDM	LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values			[paper]
TPAMI	Bayesian Temporal Factorization for Multidimensional Time Series Prediction			[paper] [code]
TNNLS	Explainable Uncertainty-Aware Convolutional Recurrent Neural Network for Irregular Medical Time Series			[paper]
IEEE Transactions on Cybernetics	Uncertainty-aware variational-recurrent imputation network for clinical time series			[paper]
Transportation Research Part C: Emerging Technologies	Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns			[paper] [code]
BCB	Concurrent Imputation and Prediction on EHR data using Bi-Directional GANs: Bi-GANs for EHR imputation and prediction			[paper] [code]
Arxiv	AutoFITS: Automatic Feature Engineering for Irregular Time Series			[paper] [code]
Arxiv	Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging			[paper] [code]

Year 2020

Venue	Title	Type	Task	Link
ICML	Learning from Irregularly-Sampled Time Series: A Missing Data Perspective			[paper] [code]
AAAI	Kriging Convolutional Networks			[paper] [code]
AAAI	DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series			[paper]
AAAI	Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values			[paper]
NeurIPS	Neural Controlled Differential Equations for Irregular Time Series			[paper] [code]
NeurIPS	Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations			[paper]
AISTATS	GP-VAE: Deep Probabilistic Time Series Imputation			[paper] [code]

Year 2019

Venue	Title	Type	Task	Link
ICLR	Interpolation-Prediction Networks for Irregularly Sampled Time Series			[paper] [code]
NeurlPS	GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series			[paper] [code]
NeurlPS	Latent ODEs for Irregularly-Sampled Time Series			[paper] [code]
ICML	Set Functions for Time Series			[paper] [code]
IJCAI	E2GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation			[paper] [code]
GigaScience	Deep learning for clustering of multivariate clinical patient trajectories with missing values			[paper] [code]
IEEE Transactions on Biomedical Engineering	Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks			[paper] [code]
Arxiv	Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)			[paper]
Arxiv	CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation			[paper] [code]

Others

Year	Venue	Title	Type	Task	Link
2018	NeurIPS	BRITS: Bidirectional Recurrent Imputation for Time Series			[paper] [code]
2018	NeurIPS	Neural Ordinary Differential Equations			[paper] [code]
2018	NeurIPS	Multivariate Time Series Imputation with Generative Adversarial Networks			[paper] [code]
2018	ICML	GAIN: Missing Data Imputation using Generative Adversarial Nets			[paper] [code]
2018	Scientific reports	Recurrent neural networks for multivariate time series with missing values			[paper] [code]
2017	KDD	Patient subtyping via time-aware lstm networks			[paper]
2016	NeurIPS	A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification			[paper]
2015	IJCAI	Imaging Time-Series to Improve Classification and Imputation			[paper] [code]