Data to 14 Oct 2026

Set Functions for Time Series

arXiv 1909.12064 ↗First posted 26 Sep 2019cs.LG stat.MLcited by 138

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Abstract (arXiv)

Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series with unaligned measurements, focusing on high scalability and data efficiency. Our method SeFT (Set Functions for Time Series) is based on recent advances in differentiable set function learning, extremely parallelizable with a beneficial memory footprint, thus scaling well to large datasets of long time series and online monitoring scenarios. Furthermore, our approach permits quantifying per-observation contributions to the classification outcome. We extensively compare our method with existing algorithms on multiple healthcare time series datasets and demonstrate that it performs competitively whilst significantly reducing runtime.

Key facts. Cited by 138 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Sep 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 12; 2023: 26; 2024: 28; 2025: 30; 2026: 29.

138 papers.

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2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 250
2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
2022SAITS: Self-Attention-based Imputation for Time Seriescited by 169
2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 111
2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 103
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
2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 55
2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 55

38 more

2021HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU Datacited by 39
2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
2021Neighborhood Contrastive Learning Applied to Online Patient Monitoringcited by 32
2021NRTSI: Non-Recurrent Time Series Imputationcited by 25
2023Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attentioncited by 25
2023Probabilistic Imputation for Time-series Classification with Missing Datacited by 22
2023Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networkcited by 20
2023Latent SDEs on Homogeneous Spacescited by 16
2025HyperIMTS: Hypergraph Neural Network for Irregular Multivariate Time Series Forecastingcited by 15
2022Temporal Label Smoothing for Early Event Predictioncited by 12
2025Time Series Language Model for Descriptive Caption Generationcited by 11
2022Learning Neural Set Functions Under the Optimal Subset Oraclecited by 10
2022Stop&Hop: Early Classification of Irregular Time Seriescited by 10
2022On the Importance of Clinical Notes in Multi-modal Learning for EHR Datacited by 10
2024TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysiscited by 10
2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
2026Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecastingcited by 9
2024TimEHR: Image-based Time Series Generation for Electronic Health Recordscited by 8
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
2022RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Educationcited by 7
2024EBES: Easy Benchmarking for Event Sequencescited 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
2025Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecastingcited by 6
2023IVP-VAE: Modeling EHR Time Series with Initial Value Problem Solverscited by 5
2023Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Seriescited by 5
2023On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Seriescited by 5
2024Dynamic Survival Analysis for Early Event Predictioncited by 5
2024EMIT- Event-Based Masked Auto Encoding for Irregular Time Seriescited by 5
2025LAST SToP For Modeling Asynchronous Time Seriescited by 5
2022DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time Seriescited by 4
2023TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcarecited by 4
2024Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamicscited by 4
2024Towards Foundation Models for Critical Care Time Seriescited by 4
2023A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimationcited by 3
2023PAITS: Pretraining and Augmentation for Irregularly-Sampled Time Seriescited by 3

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