mlsanityData to 14 Oct 2026

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

arXiv 2306.05880 ↗First posted 9 Jun 2023cs.LG cs.AIcited by 17

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Abstract (arXiv)

We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. It leverages adaptations of conditional, implicit neural representations for sequential data. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows for long-term predictions. The model provides a highly flexible and unified framework for imputation and forecasting tasks across a wide range of challenging scenarios. It achieves state-of-the-art performance on classical benchmarks and outperforms alternative time-continuous models.

Key facts. Cited by 17 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 9 Jun 2023.

Builds on

27 of 45 references.

References · 27

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  6. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  7. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  8. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  9. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  10. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  11. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  12. 2018Conditional Neural Processescited by 426
15 more
  1. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  2. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
  3. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 231
  4. 2022From data to functa: Your data point is a function and you can treat it like onecited by 187
  5. 2019Latent ODEs for Irregularly-Sampled Time Seriescited by 183
  6. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 172
  7. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 169
  8. 2022Continuous PDE Dynamics Forecasting with Implicit Neural Representationscited by 73
  9. 2019NAOMI: Non-Autoregressive Multiresolution Sequence Imputationcited by 59
  10. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
  11. 2019Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Valuescited by 37
  12. 2022Compressing multidimensional weather and climate data into neural networkscited by 31
  13. 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
  14. 2020Time series forecasting with Gaussian Processes needs priorscited by 13
  15. 2022Time-Series Anomaly Detection with Implicit Neural Representationcited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 6; 2026: 5.

17 papers.

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  1. 2024Universal Time-Series Representation Learning: A Surveycited by 30
  2. 2024DAM: Towards A Foundation Model for Time Series Forecastingcited by 10
  3. 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
  4. 2024TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detectioncited by 5
  5. 2025A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometriescited by 5
  6. 2026TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learningcited by 4
  7. 2025ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Datacited by 3
  8. 2024PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equationscited by 1
  9. 2024Implicit Neural Representation For Accurate CFD Flow Field Predictioncited by 1
  10. 2025Temporal Variational Implicit Neural Representationscited by 1
  11. 2025MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modelingcited by 1
  12. 2025Are Time-Indexed Foundation Models the Future of Time Series Imputation?cited by 1
5 more
  1. 2025Collaborative Imputation of Urban Time Series through Cross-city Meta-learningcited by 0
  2. 2026Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generationcited by 0
  3. 2026GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputationcited by 0
  4. 2026Beyond Pointwise Error: A Multi-Metric Evaluation of Spatial Climate Downscalingcited by 0
  5. 2026Fast Gradient-Based Approximation of Continuous Dynamic Time Warpingcited by 0