An experimental review on deep learning architectures for time series forecasting
P Lara-Benítez, M Carranza-García… - International journal of …, 2021 - World Scientific
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …
machine learning tasks. Deep neural networks have successfully been applied to address …
Forecast combinations: An over 50-year review
Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …
recent years, have become part of mainstream forecasting research and activities …
Statistical and Machine Learning forecasting methods: Concerns and ways forward
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 …
alternatives to statistical ones for time series forecasting. Yet, scant evidence is available …
Marine high temperature extremes amplify the impacts of climate change on fish and fisheries
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 …
detrimental impacts on marine biodiversity, ecosystem functions, and services. However …
[HTML][HTML] M5 accuracy competition: Results, findings, and conclusions
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 …
two parallel challenges in the latest M competition with the aim of advancing the theory and …
Artificial intelligence in the battle against coronavirus (COVID-19): a survey and future research directions
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 …
numerous success stories. AI has also contributed to dealing with the coronavirus disease …
Machine learning in energy economics and finance: A review
Abstract Machine learning (ML) is generating new opportunities for innovative research in
energy economics and finance. We critically review the burgeoning literature dedicated to …
energy economics and finance. We critically review the burgeoning literature dedicated to …
A survey on machine learning models for financial time series forecasting
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 …
facilitate FTS forecasting has been highly pursued for decades. Despite major related …
Computational intelligence and financial markets: A survey and future directions
RC Cavalcante, RC Brasileiro, VLF Souza… - Expert Systems with …, 2016 - Elsevier
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 …
modern society. In these kinds of markets, information is an invaluable asset. However, with …
Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach
K Bandara, C Bergmeir, S Smyl - Expert systems with applications, 2020 - Elsevier
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 …
quantities of similar time series are available. Forecasting time series in these domains with …