mlsanityData to 7 Oct 2026

Continuous Time Evidential Distributions for Irregular Time Series

arXiv 2307.13503 ↗First posted 25 Jul 2023cs.LG stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

Prevalent in many real-world settings such as healthcare, irregular time series are challenging to formulate predictions from. It is difficult to infer the value of a feature at any given time when observations are sporadic, as it could take on a range of values depending on when it was last observed. To characterize this uncertainty we present EDICT, a strategy that learns an evidential distribution over irregular time series in continuous time. This distribution enables well-calibrated and flexible inference of partially observed features at any time of interest, while expanding uncertainty temporally for sparse, irregular observations. We demonstrate that EDICT attains competitive performance on challenging time series classification tasks and enabling uncertainty-guided inference when encountering noisy data.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 66 references that are in the map. First posted on arXiv 25 Jul 2023.

Builds on

37 of 66 references.

References · 37

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,335
  4. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  5. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  6. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  7. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  8. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 548
  9. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  10. 2019Measuring Calibration in Deep Learningcited by 443
  11. 2019Deep Evidential Regressioncited by 354
  12. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
25 more
  1. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 288
  2. 2022On Neural Differential Equationscited by 264
  3. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 230
  4. 2019GP-VAE: Deep Probabilistic Time Series Imputationcited by 171
  5. 2020Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Countscited by 163
  6. 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
  7. 2020Liquid Time-constant Networkscited by 160
  8. 2019Neural Jump Stochastic Differential Equationscited by 147
  9. 2020Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flowscited by 142
  10. 2019Set Functions for Time Seriescited by 137
  11. 2019MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIIcited by 121
  12. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 101
  13. 2019Interpolation-Prediction Networks for Irregularly Sampled Time Seriescited by 89
  14. 2016Modeling Missing Data in Clinical Time Series with RNNscited by 80
  15. 2021Closed-form Continuous-time Neural Modelscited by 65
  16. 2019Quality of Uncertainty Quantification for Bayesian Neural Network Inferencecited by 58
  17. 2021Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamicscited by 46
  18. 2020Learning from Irregularly-Sampled Time Series: A Missing Data Perspectivecited by 40
  19. 2017Discrete Event, Continuous Time RNNscited by 31
  20. 2020Regression Prior Networkscited by 28
  21. 2022Copula Conformal Prediction for Multi-step Time Series Forecastingcited by 28
  22. 2021Multivariate Deep Evidential Regressioncited by 23
  23. 2022Predicting the impact of treatments over time with uncertainty aware neural differential equationscited by 23
  24. 2021Uncertainty Quantification in Neural Differential Equationscited by 3
  25. 2023Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networkscited by 2

Built on by

Not cited yet.