mlsanityData to 7 Oct 2026

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows

arXiv 2002.10516 ↗First posted 24 Feb 2020cs.LG stat.MLcited by 37

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Abstract (arXiv)

Normalizing flows transform a simple base distribution into a complex target distribution and have proved to be powerful models for data generation and density estimation. In this work, we propose a novel type of normalizing flow driven by a differential deformation of the Wiener process. As a result, we obtain a rich time series model whose observable process inherits many of the appealing properties of its base process, such as efficient computation of likelihoods and marginals. Furthermore, our continuous treatment provides a natural framework for irregular time series with an independent arrival process, including straightforward interpolation. We illustrate the desirable properties of the proposed model on popular stochastic processes and demonstrate its superior flexibility to variational RNN and latent ODE baselines in a series of experiments on synthetic and real-world data.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Feb 2020.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 9; 2024: 6; 2025: 4; 2026: 3.

37 papers.

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  1. 2022SAITS: Self-Attention-based Imputation for Time Seriescited by 167
  2. 2021Neural SDEs as Infinite-Dimensional GANscited by 159
  3. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 101
  4. 2021Efficient and Accurate Gradients for Neural SDEscited by 68
  5. 2021Neural ODE Processescited by 57
  6. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 43
  7. 2024Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Seriescited by 42
  8. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
  9. 2022Deep Latent State Space Models for Time-Series Generationcited by 37
  10. 2023Non-adversarial training of Neural SDEs with signature kernel scorescited by 36
  11. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  12. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
25 more
  1. 2021Multi-Asset Spot and Option Market Simulationcited by 17
  2. 2023TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformerscited by 13
  3. 2021Continuous Latent Process Flowscited by 10
  4. 2022MIMO-GAN: Generative MIMO Channel Modelingcited by 9
  5. 2023Bayesian Gaussian Process ODEs via Double Normalizing Flowscited by 7
  6. 2024Modeling Unknown Stochastic Dynamical System Subject to External Excitationcited by 7
  7. 2022Neural Piecewise-Constant Delay Differential Equationscited by 4
  8. 2021Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modelingcited by 3
  9. 2023Time-Conditioned Generative Modeling of Object-Centric Representations for Video Decomposition and Predictioncited by 3
  10. 2024Data Augmentation for Multivariate Time Series Classification: An Experimental Studycited by 3
  11. 2024Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysiscited by 3
  12. 2025CTBench: Cryptocurrency Time Series Generation Benchmarkcited by 3
  13. 2025TSGM: Regular and Irregular Time-series Generation using Score-based Generative Modelscited by 3
  14. 2025Diffusion differentiable resamplingcited by 2
  15. 2026Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observationscited by 2
  16. 2024Probabilistic Forecasting of Irregular Time Series via Conditional Flowscited by 1
  17. 2025Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Networkcited by 1
  18. 2022Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehiclescited by 0
  19. 2022Continuous-time Particle Filtering for Latent Stochastic Differential Equationscited by 0
  20. 2023Directed Chain Generative Adversarial Networkscited by 0
  21. 2023HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoisingcited by 0
  22. 2023Time-changed normalizing flows for accurate SDE modelingcited by 0
  23. 2024Marginalization Consistent Probabilistic Forecasting of Irregular Time Series via Mixture of Separable flowscited by 0
  24. 2026Generation of synthetic delay time series for air transport applicationscited by 0
  25. 2026Robustly Invertible Nonlinear Dynamics and the BiLipREN: From Inversion-Based Control to Generative Trajectory Modellingcited by 0