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Related Experiment Video

Updated: Oct 9, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks.

Chen Qiu^1,2, Stephan Mandt³, Maja Rudolph⁴

¹Bosch Center for AI, 71272 Renningen, Germany.

Entropy (Basel, Switzerland)

|December 24, 2021

Summary

Deep probabilistic time series forecasting models can predict unrealistic outcomes due to mode-averaging. Variational Dynamic Mixtures (VDM) offer a novel solution for multi-modal forecasting, improving accuracy on complex datasets.

Keywords:

sequential latent variable models time series forecasting variational inference

Related Experiment Videos

Last Updated: Oct 9, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

Machine Learning
Time Series Analysis
Probabilistic Modeling

Background:

Deep probabilistic models are crucial for time series forecasting.
Current generative models often suffer from limited inference, leading to mode-averaged predictions.
Mode-averaging results in unphysical forecasts for multi-modal real-world sequences.

Purpose of the Study:

To address the limitations of mode-averaging in deep probabilistic time series forecasting.
To introduce a novel variational family for inferring sequential latent variables.
To improve the capture of multi-modality in time series data.

Main Methods:

Developed Variational Dynamic Mixtures (VDM), a new variational family.
VDM utilizes a mixture density network for approximate posterior at each time step.
Employs a recurrent architecture to propagate multiple samples for posterior approximation.

Main Results:

VDM provides an expressive multi-modal posterior approximation.
Empirical studies demonstrate VDM's superior performance.
VDM outperforms competing methods on highly multi-modal datasets across various domains.

Conclusions:

VDM effectively captures multi-modal dynamics in time series forecasting.
The proposed method overcomes limitations of existing inference models.
VDM represents a significant advancement for probabilistic time series forecasting.