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

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 …

Extreme temperature events have occurred in all ocean basins in the past two decades with
detrimental impacts on marine biodiversity, ecosystem functions, and services. However …

In this study, we present the results of the M5 “Accuracy” competition, which was the first of
two parallel challenges in the latest M competition with the aim of advancing the theory and …

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with
numerous success stories. AI has also contributed to dealing with the coronavirus disease …

Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …

Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to
facilitate FTS forecasting has been highly pursued for decades. Despite major related …

Financial markets play an important role on the economical and social organization of
modern society. In these kinds of markets, information is an invaluable asset. However, with …

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 …