Numerous deep learning architectures have been developed to accommodate the diversity
of time-series datasets across different domains. In this article, we survey common encoder …

Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

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 …

Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

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 …

Forecasting high-dimensional time series plays a crucial role in many applications such as
demand forecasting and financial predictions. Modern datasets can have millions of …

Generative modeling of multivariate time series has remained challenging partly due to the
complex, non-deterministic dynamics across long-distance timesteps. In this paper, we …

Multi-variate time series (MTS) data is a ubiquitous class of data abstraction in the real
world. Any instance of MTS is generated from a hybrid dynamical system with their specific …

Global methods that fit a single forecasting method to all time series in a set have recently
shown surprising accuracy, even when forecasting large groups of heterogeneous time …