Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Missing data in time series is a pervasive problem that puts obstacles in the way of
advanced analysis. A popular solution is imputation, where the fundamental challenge is to …

Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

We focus on solving the univariate times series point forecasting problem using deep
learning. We propose a deep neural architecture based on backward and forward residual …

We present a novel approach to probabilistic time series forecasting that combines state
space models with deep learning. By parametrizing a per-time-series linear state space …

We propose a novel method for imputing missing data by adapting the well-known
Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative …

Many approaches have been proposed for time series forecasting, in light of its significance
in wide applications including business demand prediction. However, the existing methods …

Spatiotemporal forecasting has various applications in neuroscience, climate and
transportation domain. Traffic forecasting is one canonical example of such learning task …