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PrimeNet: Pre-training for Irregular Multivariate Time Series
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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An Agent-Based Approach to Care in Independent Living
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Time series extrinsic regression
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Predicting healthcare trajectories from medical records: A deep learning approach
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Recurrent Neural Networks for Multivariate Time Series with Missing Values
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MIMIC-III, a freely accessible critical care database
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Momentum Contrast for Unsupervised Visual Representation Learning
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Unsupervised pattern discovery in electronic health care data using probabilistic clustering models
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Real-Time Principal Component Analysis
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Unleashing the Power of Shared Label Structures for Human Activity Recognition
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DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series
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TS2Vec: Towards Universal Representation of Time Series
inferredcites →https://doi.org/10.18653/v1/2022.acl-long.426openalex-snapshot
UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining
inferredcites →https://doi.org/10.18653/v1/2022.emnlp-main.71openalex-snapshot
Fine-grained Contrastive Learning for Relation Extraction
inferredcites →https://doi.org/10.24963/ijcai.2019/607openalex-snapshot
ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling
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Set Functions for Time Series
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Representation Learning with Contrastive Predictive Coding
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Unsupervised Scalable Representation Learning for Multivariate Time Series
inferredcites →https://doi.org/10.48550/arxiv.2002.05709openalex-snapshot
A Simple Framework for Contrastive Learning of Visual Representations
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Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
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Spiral Time: A Geometric Reframing of Temporal Structure and Its Applications in Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Forecasting Irregularly Sampled Time Series with Transformer Encoders
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DITS: Double Imputation for Enhanced Irregular Multivariate Time Series Classification
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TS-TFMAE: time series self-supervised learning with time–frequency masked autoencoders
inferred← citeshttps://doi.org/10.1016/j.ins.2026.123094openalex-snapshot
SFAFormer: Sampling Frequency-Aware Transformer Specialized for Unsupervised Anomaly Detection in Irregular Multivariate Time Series
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113825openalex-snapshot
Multi-Level Transfer Learning for irregular clinical time series prediction
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Accurate prediction of intradialytic hypo- and hypertension in haemodialysis patients: the dual-attention Transformer model
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HC-Monet: Hierarchical Continuous Masked Operator Network with a Shared Spectral Mixture Time Kernel for Irregular Time Series
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EMIT - Event-Based Masked Auto Encoding for Irregular Time Series
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Dynamic Modeling of Patient Vital Signs: Leveraging Markov Chain Principles with Neural Networks for Irregular Time-Series Prediction
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A Survey of Forecasting Methods for Irregular Time Series
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DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series
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TrajGPT: Irregular Time-Series Representation Learning of Health Trajectory
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CrossHAR: Generalizing Cross-dataset Human Activity Recognition via Hierarchical Self-Supervised Pretraining
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EBES: Easy Benchmarking for Event Sequences
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A Hybrid Approach for Irregular-Time Series Prediction Using Electronic Health Records: An Intensive Care Unit Mortality Case Study
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Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
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Efficient Anomaly Detection of Irregular Sequences in Ct-Echo Model Space
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Neuro-Markovian Approach to Forecasting Patient Vital Signs from Irregular Time-Series
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MS-HNODE: Time-Aware Multi-branch Neural ODEs for Heterogeneous Latent Dynamics in Irregularly Sampled Multivariate Time Series
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Non-temporal tree-based models outperform temporal deep learning models in the prediction of chemotherapy-induced side effects from longitudinal laboratory data
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Learning neural representation for time series
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