Data to 14 Oct 2026

Interpolation-Prediction Networks for Irregularly Sampled Time Series

arXiv 1909.07782 ↗First posted 13 Sep 2019cs.LG stat.MLcited by 89

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Abstract (arXiv)

In this paper, we present a new deep learning architecture for addressing the problem of supervised learning with sparse and irregularly sampled multivariate time series. The architecture is based on the use of a semi-parametric interpolation network followed by the application of a prediction network. The interpolation network allows for information to be shared across multiple dimensions of a multivariate time series during the interpolation stage, while any standard deep learning model can be used for the prediction network. This work is motivated by the analysis of physiological time series data in electronic health records, which are sparse, irregularly sampled, and multivariate. We investigate the performance of this architecture on both classification and regression tasks, showing that our approach outperforms a range of baseline and recently proposed models.

Key facts. Cited by 89 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Sep 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 12; 2023: 20; 2024: 17; 2025: 9; 2026: 18.

89 papers.

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2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 111
2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
2024FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusioncited by 68
2021Closed-form Continuous-time Neural Modelscited by 65
2023Asynchrony-Robust Collaborative Perception via Bird's Eye View Flowcited by 58
2024A Closer Look at AUROC and AUPRC under Class Imbalancecited by 57
2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 55
2021Neural Point Process for Learning Spatiotemporal Event Dynamicscited by 42
2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40

38 more

2023Personalized Federated Learning under Mixture of Distributionscited by 37
2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
2024Universal Time-Series Representation Learning: A Surveycited by 30
2023Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attentioncited by 25
2021Variable-rate discrete representation learningcited by 24
2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 22
2025HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecastingcited by 15
2021TE-ESN: Time Encoding Echo State Network for Prediction Based on Irregularly Sampled Time Series Datacited by 10
2022Stop&Hop: Early Classification of Irregular Time Seriescited by 10
2024Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Seriescited by 10
2024TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysiscited by 10
2024SMART: Towards Pre-trained Missing-Aware Model for Patient Health Status Predictioncited by 8
2025ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecastingcited by 8
2024EBES: Easy Benchmarking for Event Sequencescited by 7
2026GlucoFM: A Dual-Stream Foundation Model for Continuous Glucose Monitoringcited by 7
2022Tripletformer for Probabilistic Interpolation of Irregularly sampled Time Seriescited by 6
2023Revisiting the Encoding of Satellite Image Time Seriescited by 6
2024CTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysiscited by 6
2022DeepJoint: Robust Survival Modelling Under Clinical Presence Shiftcited by 5
2023Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Seriescited by 5
2024EMIT- Event-Based Masked Auto Encoding for Irregular Time Seriescited by 5
2022What is Next when Sequential Prediction Meets Implicitly Hard Interaction?cited by 4
2022DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Seriescited by 4
2022NIERT: Accurate Numerical Interpolation through Unifying Scattered Data Representations using Transformer Encodercited by 4
2024Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamicscited by 4
2024TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Recordscited by 3
2024Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Datacited by 3
2022Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Techniquecited by 2
2022Irregularly-Sampled Time Series Modeling with Spline Networkscited by 2
2023Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networkscited by 2
2023Tree Cross Attentioncited by 2
2023Compatible Transformer for Irregularly Sampled Multivariate Time Seriescited by 2
2023Know Thy Neighbors: A Graph Based Approach for Effective Sensor-Based Human Activity Recognition in Smart Homescited by 2
2024Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Seriescited by 2
2024Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAVcited by 2
2025Bi-Axial Transformers: Addressing the Increasing Complexity of EHR Classificationcited by 2
2025Mind the Missing: Variable-Aware Representation Learning for Irregular EHR Time Series using Large Language Modelscited by 2

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