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Angel E. Muñoz-Zavala, Jorge E. Macías-Díaz, Daniel Alba-Cuéllar and José A. Guerrero-Díaz-de-León
This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN arc...
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Davide Fronzi, Gagan Narang, Alessandro Galdelli, Alessandro Pepi, Adriano Mancini and Alberto Tazioli
Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at var...
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Konstantinos Psychogyios, Andreas Papadakis, Stavroula Bourou, Nikolaos Nikolaou, Apostolos Maniatis and Theodore Zahariadis
The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individual...
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Alexios Lekidis, Angelos Georgakis, Christos Dalamagkas and Elpiniki I. Papageorgiou
The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which ma...
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Devon Barrow, Antonija Mitrovic, Jay Holland, Mohammad Ali and Nikolaos Kourentzes
In forecasting research, the focus has largely been on decision support systems for enhancing performance, with fewer studies in learning support systems. As a remedy, Intelligent Tutoring Systems (ITSs) offer an innovative solution in that they provide ...
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José Francisco Lima, Fernanda Catarina Pereira, Arminda Manuela Gonçalves and Marco Costa
Linear models, seasonal autoregressive integrated moving average (SARIMA) models, and state-space models have been widely adopted to model and forecast economic data. While modeling using linear models and SARIMA models is well established in the literat...
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Xiaoou Li
This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative ...
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Fahim Sufi
In the face of escalating cyber threats that have contributed significantly to global economic losses, this study presents a comprehensive dataset capturing the multifaceted nature of cyber-attacks across 225 countries over a 14-month period from October...
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Louis Closson, Christophe Cérin, Didier Donsez and Jean-Luc Baudouin
This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the ac...
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George Westergaard, Utku Erden, Omar Abdallah Mateo, Sullaiman Musah Lampo, Tahir Cetin Akinci and Oguzhan Topsakal
Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models wit...
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