3 research outputs found
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
Integrating deep learning with latent state space models has the potential to
yield temporal models that are powerful, yet tractable and interpretable.
Unfortunately, current models are not designed to handle missing data or
multiple data modalities, which are both prevalent in real-world data. In this
work, we introduce a factorized inference method for Multimodal Deep Markov
Models (MDMMs), allowing us to filter and smooth in the presence of missing
data, while also performing uncertainty-aware multimodal fusion. We derive this
method by factorizing the posterior p(z|x) for non-linear state space models,
and develop a variational backward-forward algorithm for inference. Because our
method handles incompleteness over both time and modalities, it is capable of
interpolation, extrapolation, conditional generation, label prediction, and
weakly supervised learning of multimodal time series. We demonstrate these
capabilities on both synthetic and real-world multimodal data under high levels
of data deletion. Our method performs well even with more than 50% missing
data, and outperforms existing deep approaches to inference in latent time
series.Comment: 8 pages, 4 figures, accepted to AAAI 2020, code available at:
https://github.com/ztangent/multimodal-dm
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Forecasting on sparse multivariate time series (MTS) aims to model the
predictors of future values of time series given their incomplete past, which
is important for many emerging applications. However, most existing methods
process MTS's individually, and do not leverage the dynamic distributions
underlying the MTS's, leading to sub-optimal results when the sparsity is high.
To address this challenge, we propose a novel generative model, which tracks
the transition of latent clusters, instead of isolated feature representations,
to achieve robust modeling. It is characterized by a newly designed dynamic
Gaussian mixture distribution, which captures the dynamics of clustering
structures, and is used for emitting timeseries. The generative model is
parameterized by neural networks. A structured inference network is also
designed for enabling inductive analysis. A gating mechanism is further
introduced to dynamically tune the Gaussian mixture distributions. Extensive
experimental results on a variety of real-life datasets demonstrate the
effectiveness of our method.Comment: This paper is accepted by AAAI 202