mlsanityData to 14 Oct 2026

HyperTime: Implicit Neural Representation for Time Series

arXiv 2208.05836 ↗First posted 11 Aug 2022cs.LGcited by 24

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Abstract (arXiv)

Implicit neural representations (INRs) have recently emerged as a powerful tool that provides an accurate and resolution-independent encoding of data. Their robustness as general approximators has been shown in a wide variety of data sources, with applications on image, sound, and 3D scene representation. However, little attention has been given to leveraging these architectures for the representation and analysis of time series data. In this paper, we analyze the representation of time series using INRs, comparing different activation functions in terms of reconstruction accuracy and training convergence speed. We show how these networks can be leveraged for the imputation of time series, with applications on both univariate and multivariate data. Finally, we propose a hypernetwork architecture that leverages INRs to learn a compressed latent representation of an entire time series dataset. We introduce an FFT-based loss to guide training so that all frequencies are preserved in the time series. We show that this network can be used to encode time series as INRs, and their embeddings can be interpolated to generate new time series from existing ones. We evaluate our generative method by using it for data augmentation, and show that it is competitive against current state-of-the-art approaches for augmentation of time series.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 11 Aug 2022.

Builds on

34 of 58 references.

References · 34

  1. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  2. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 2016HyperNetworkscited by 1,027
  6. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  7. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  8. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  9. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
  10. 2018Meta-Learning with Latent Embedding Optimizationcited by 476
  11. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  12. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
22 more
  1. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  2. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  3. 2019Continual learning with hypernetworkscited by 273
  4. 2020Learned Initializations for Optimizing Coordinate-Based Neural Representationscited by 242
  5. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  6. 2020MetaSDF: Meta-learning Signed Distance Functionscited by 214
  7. 2017SMASH: One-Shot Model Architecture Search through HyperNetworkscited by 202
  8. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  9. 2020Adversarial Generation of Continuous Imagescited by 135
  10. 2018Graph HyperNetworks for Neural Architecture Searchcited by 132
  11. 2020An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networkscited by 128
  12. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  13. 2020Image Generators with Conditionally-Independent Pixel Synthesiscited by 106
  14. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
  15. 2019Deep Meta Functionals for Shape Representationcited by 53
  16. 2021Seeing Implicit Neural Representations as Fourier Seriescited by 50
  17. 2020Spatially-Adaptive Pixelwise Networks for Fast Image Translationcited by 45
  18. 2020Time Series Data Imputation: A Survey on Deep Learning Approachescited by 34
  19. 2021Towards Realistic Market Simulations: a Generative Adversarial Networks Approachcited by 29
  20. 2021Neural BRDF Representation and Importance Samplingcited by 28
  21. 2022Time-Series Anomaly Detection with Implicit Neural Representationcited by 10
  22. 2021Adaptive Weighting Scheme for Automatic Time-Series Data Augmentationcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 7; 2025: 10; 2026: 2.

24 papers.

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  1. 2024Universal Time-Series Representation Learning: A Surveycited by 30
  2. 2023Modality-Agnostic Variational Compression of Implicit Neural Representationscited by 22
  3. 2023Polynomial Implicit Neural Representations For Large Diverse Datasetscited by 18
  4. 2023Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representationscited by 17
  5. 2024Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patchingcited by 16
  6. 2023Hypernetworks build Implicit Neural Representations of Soundscited by 11
  7. 2024TimeLDM: Latent Diffusion Model for Unconditional Time Series Generationcited by 10
  8. 2025Towards Identifiability of Hierarchical Temporal Causal Representation Learningcited by 9
  9. 2025LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputationcited by 8
  10. 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
  11. 2026FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmographycited by 5
  12. 2023MADS: Modulated Auto-Decoding SIREN for time series imputationcited by 3
12 more
  1. 2025ARC: Anchored Representation Clouds for High-Resolution INR Classificationcited by 3
  2. 2025ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Datacited by 3
  3. 2025Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matchingcited by 2
  4. 2024PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equationscited by 1
  5. 2024Forecasting with Hyper-Treescited by 1
  6. 2024Federated Time Series Generation on Feature and Temporally Misaligned Datacited by 1
  7. 2025Temporal Variational Implicit Neural Representationscited by 1
  8. 2025Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworkscited by 1
  9. 2025TabINR: An Implicit Neural Representation Framework for Tabular Data Imputationcited by 1
  10. 2025Collaborative Imputation of Urban Time Series through Cross-city Meta-learningcited by 0
  11. 2025HN-MVTS: HyperNetwork-based Multivariate Time Series Forecastingcited by 0
  12. 2026Short-term electricity load forecasting with multi-frequency reconstruction diffusioncited by 0