mlsanityData to 14 Oct 2026

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline

arXiv 2505.11250 ↗First posted 16 May 2025cs.LGcited by 55

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Abstract (arXiv)

The forecasting of irregular multivariate time series (IMTS) is crucial in key areas such as healthcare, biomechanics, climate science, and astronomy. However, achieving accurate and practical predictions is challenging due to two main factors. First, the inherent irregularity and data missingness in irregular time series make modeling difficult. Second, most existing methods are typically complex and resource-intensive. In this study, we propose a general framework called APN to address these challenges. Specifically, we design a novel Time-Aware Patch Aggregation (TAPA) module that achieves adaptive patching. By learning dynamically adjustable patch boundaries and a time-aware weighted averaging strategy, TAPA transforms the original irregular sequences into high-quality, regularized representations in a channel-independent manner. Additionally, we use a simple query module to effectively integrate historical information while maintaining the model's efficiency. Finally, predictions are made by a shallow MLP. Experimental results on multiple real-world datasets show that APN outperforms existing state-of-the-art methods in both efficiency and accuracy.

Key facts. Cited by 55 papers in the map (data through 14 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

31 of 47 references.

References · 31

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  7. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  8. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  9. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 506
  10. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  11. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 249
  12. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
19 more
  1. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 231
  2. 2019Set Functions for Time Seriescited by 138
  3. 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 127
  4. 2021Graph-Guided Network for Irregularly Sampled Multivariate Time Seriescited by 111
  5. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 107
  6. 2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 106
  7. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 103
  8. 2021Neural Flows: Efficient Alternative to Neural ODEscited by 101
  9. 2024ContiFormer: Continuous-Time Transformer for Irregular Time Series Modelingcited by 63
  10. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  11. 2023Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Seriescited by 55
  12. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 49
  13. 2023Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Seriescited by 29
  14. 2024Are Self-Attentions Effective for Time Series Forecasting?cited by 19
  15. 2023CrossFormer++: A Versatile Vision Transformer Hinging on Cross-scale Attentioncited by 18
  16. 2024SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputationcited by 18
  17. 2024Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Seriescited by 15
  18. 2024Temporal Graph ODEs for Irregularly-Sampled Time Seriescited by 11
  19. 2024TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysiscited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 14; 2026: 41.

55 papers.

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  1. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 60
  2. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 45
  3. 2025OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrievalcited by 40
  4. 2025Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspectivecited by 39
  5. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 29
  6. 2026FastVMT: Eliminating Redundancy in Video Motion Transfercited by 23
  7. 2025Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecastingcited by 20
  8. 2026GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variablescited by 13
  9. 2025CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentationcited by 12
  10. 2026Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecastingcited by 9
  11. 2025ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecastingcited by 8
  12. 2026Unlocking the Value of Text: Event-Driven Reasoning and Multi-Level Alignment for Time Series Forecastingcited by 5
38 more
  1. 2026TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecastingcited by 5
  2. 2025Diffusion-Based Image Editing: An Unforeseen Adversary to Robust Invisible Watermarkscited by 3
  3. 2025FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecastingcited by 3
  4. 2026PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecastingcited by 3
  5. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  6. 2025Task-Aware Mixture-of-Experts for Time Series Analysiscited by 2
  7. 2025Entropy Guided Dynamic Patch Segmentation for Time Series Transformerscited by 2
  8. 2026TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generationcited by 2
  9. 2026MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Modelscited by 2
  10. 2026Vanishing Watermarks: Diffusion-Based Image Editing Undermines Robust Invisible Watermarkingcited by 2
  11. 2026TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patchingcited by 2
  12. 2026WPBench: A Comprehensive Benchmark for Wind Power Forecastingcited by 2
  13. 2026Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failurescited by 1
  14. 2026SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacementcited by 1
  15. 2026ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologiescited by 1
  16. 2026MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMscited by 1
  17. 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offscited by 1
  18. 2026Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecastingcited by 1
  19. 2026What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependenciescited by 1
  20. 2026L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecastingcited by 1
  21. 2026Existence Precedes Value: Joint Modeling of Observational Existence and Evolving States in Time Series Forecastingcited by 1
  22. 2026Enhancing Irregular Time Series Forecasting with Continuous-Time Modeling Frameworkcited by 1
  23. 2026QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalitiescited by 1
  24. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  25. 2025Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustnesscited by 0
  26. 2026BSAT: B-Spline Adaptive Tokenizer for Long-Term Time Series Forecastingcited by 0
  27. 2026SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecastingcited by 0
  28. 2026When Denoising Becomes Unsigning: Theoretical and Empirical Analysis of Watermark Fragility Under Diffusion-Based Image Editingcited by 0
  29. 2026Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarkingcited by 0
  30. 2026CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Modelscited by 0
  31. 2026TIDES: Implicit Time-Awareness in Selective State Space Modelscited by 0
  32. 2026LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Schedulingcited by 0
  33. 2026On the Controllability-Fidelity Frontier in Diffusion Editingcited by 0
  34. 2026Nonlocal Bayesian Modeling of Continuous Spatio-Temporal Dynamicscited by 0
  35. 2026Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functionscited by 0
  36. 2026Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecastingcited by 0
  37. 2026MUSE: Dependency-Aware Adaptation of a Frozen Vision Backbone for Multivariate Time Series Forecastingcited by 0
  38. 2026Aurora-X: Built for Extreme Time Series Forecastingcited by 0

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