RegFlow: Probabilistic Flow-based Regression for Future Prediction

• URL: http://arxiv.org/abs/2011.14620v1
• Date: Mon, 30 Nov 2020 08:45:37 GMT
• Title: RegFlow: Probabilistic Flow-based Regression for Future Prediction
• Authors: Maciej Zi\k{e}ba, Marcin Przewi\k{e}\'zlikowski, Marek \'Smieja, Jacek Tabor, Tomasz Trzcinski, Przemys{\l}aw Spurek
• Abstract summary: We introduce a robust and flexible probabilistic framework that allows to model future predictions with virtually no constrains regarding the modality or underlying probability distribution. The resulting method dubbed RegFlow achieves state-of-the-art results on several benchmark datasets, outperforming competing approaches by a significant margin.
• Score: 21.56753543722155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans. Existing approaches provide results under strong assumptions concerning unimodality of future states, or, at best, assuming specific probability distributions that often poorly fit to real-life conditions. In this work we introduce a robust and flexible probabilistic framework that allows to model future predictions with virtually no constrains regarding the modality or underlying probability distribution. To achieve this goal, we leverage a hypernetwork architecture and train a continuous normalizing flow model. The resulting method dubbed RegFlow achieves state-of-the-art results on several benchmark datasets, outperforming competing approaches by a significant margin.

Related papers

• Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series [7.200880964149064]
  Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models. Applying conformal prediction to probabilistic generative models, such as Normalising Flows is not straightforward. This work proposes a novel method to conformalise conditional normalising flows, specifically addressing the problem of obtaining prediction regions.
  arXiv  Detail & Related papers  (2024-11-26T02:19:13Z)
• Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes [18.344934424278048]
  We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. We show that the drift and the diffusion coefficients of this SDE can be adjusted after training, and that a specific choice that minimizes the impact of the estimation error gives a F"ollmer process.
  arXiv  Detail & Related papers  (2024-03-20T16:33:06Z)
• On the Efficient Marginalization of Probabilistic Sequence Models [3.5897534810405403]
  This dissertation focuses on using autoregressive models to answer complex probabilistic queries. We develop a class of novel and efficient approximation techniques for marginalization in sequential models that are model-agnostic.
  arXiv  Detail & Related papers  (2024-03-06T19:29:08Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems [49.75149094527068]
  We show that diffusion models can be adapted to make predictions and provide uncertainty quantification for chaotic dynamical systems. We develop a probabilistic approximation scheme for the conditional score function which converges to the true distribution as the noise level decreases. We are able to sample conditionally on nonlinear userdefined events at inference time, and matches data statistics even when sampling from the tails of the distribution.
  arXiv  Detail & Related papers  (2023-06-13T03:42:03Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Uncertainty estimation of pedestrian future trajectory using Bayesian approximation [137.00426219455116]
  Under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. The authors propose to quantify uncertainty during forecasting using approximation which deterministic approaches fail to capture. The effect of dropout weights and long-term prediction on future state uncertainty has been studied.
  arXiv  Detail & Related papers  (2022-05-04T04:23:38Z)
• Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization [5.5643498845134545]
  We use generative neural networks to parametrize distributions on high-dimensional spaces by transforming draws from a latent variable. We train generative networks to minimize a predictive-sequential (or prequential) scoring rule on a recorded temporal sequence of the phenomenon of interest. Our method outperforms state-of-the-art adversarial approaches, especially in probabilistic calibration.
  arXiv  Detail & Related papers  (2021-12-15T15:51:12Z)
• Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression [91.3373131262391]
  Uncertainty is the only certainty there is. Traditionally, the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. How to model the uncertainty within the present-day technologies for regression remains an open issue.
  arXiv  Detail & Related papers  (2021-03-25T06:56:09Z)
• FloMo: Tractable Motion Prediction with Normalizing Flows [0.0]
  We model motion prediction as a density estimation problem with a normalizing flow between a noise sample and the future motion distribution. Our model, named FloMo, allows likelihoods to be computed in a single network pass and can be trained directly with maximum likelihood estimation. Our method achieves state-of-the-art performance on three popular prediction datasets, with a significant gap to most competing models.
  arXiv  Detail & Related papers  (2021-03-05T11:35:27Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.