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 …

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

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

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 …

The use of wind energy plays a vital role in society owing to its economic and environmental
importance. Knowing the wind power generation within a specific time window is useful for …

Forecasting is required in many situations. Stocking an inventory may require forecasts of
demand months in advance. Telecommunication routing requires traffic forecasts a few …

In the last decades, the world's energy consumption has increased rapidly due to
fundamental changes in the industry and economy. In such terms, accurate demand …

With the advent of Big Data, nowadays in many applications databases containing large
quantities of similar time series are available. Forecasting time series in these domains with …

Forecasting models that are trained across sets of many time series, known as Global
Forecasting Models (GFM), have shown recently promising results in forecasting …