Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

by   Yinjun Wu, et al.

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.


page 1

page 2

page 3

page 4


Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)

Generative Adversarial Networks (GANs) have gained significant attention...

Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks

Supervised learning, while deployed in real-life scenarios, often encoun...

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

Modeling the spatiotemporal nature of the spread of infectious diseases ...

ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models

Multivariate time series prediction has attracted a lot of attention bec...

Generative Models for Periodicity Detection in Noisy Signals

We introduce a new periodicity detection algorithm for binary time serie...

Compositional Model based Fisher Vector Coding for Image Classification

Deriving from the gradient vector of a generative model of local feature...

Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits

This paper examines deposits of individuals ("retail") and large compani...