In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

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 …

Regression analysis makes up a large part of supervised machine learning, and consists of
the prediction of a continuous independent target from a set of other predictor variables. The …

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 …

What will be the global impact of the novel coronavirus (COVID-19)? Answering this
question requires accurate forecasting the spread of confirmed cases as well as analysis of …

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

The M4 Competition follows on from the three previous M competitions, the purpose of which
was to learn from empirical evidence both how to improve the forecasting accuracy and how …

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 …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

Machine Learning (ML) methods have been proposed in the academic literature as
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …