mlsanityData to 14 Oct 2026

Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics

arXiv 2410.12257 ↗First posted 16 Oct 2024cs.LGcited by 4

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Abstract (arXiv)

Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4%, 94.9%, 60% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-IrregularTimeSeries.git

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 16 Oct 2024.

Builds on

27 of 45 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Neural Ordinary Differential Equationscited by 3,779
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  5. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  6. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  7. 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
  8. 2017Dilated Residual Networkscited by 294
  9. 2017Multitask learning and benchmarking with clinical time series datacited by 290
  10. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  11. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
  12. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 182
15 more
  1. 2019Set Functions for Time Seriescited by 138
  2. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 111
  3. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 103
  4. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 101
  5. 2021Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Seriescited by 96
  6. 2019Interpolation-Prediction Networks for Irregularly Sampled Time Seriescited by 89
  7. 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
  8. 2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 55
  9. 2024Reliable Conflictive Multi-View Learningcited by 45
  10. 2022Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modelingcited by 33
  11. 2017Discrete Event, Continuous Time RNNscited by 31
  12. 2020A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Datacited by 31
  13. 2021Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Seriescited by 30
  14. 2023Temporal Graph Neural Networks for Irregular Datacited by 7
  15. 2023SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Seriescited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gatecited by 3
  2. 2025Lifting Manifolds to Mitigate Pseudo-Alignment in LLM4TScited by 2
  3. 2026DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classificationcited by 0
  4. 2026GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Carecited by 0