Titlebook: Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks; Patricia Melin,Martha Ramirez,Oscar Castillo B

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Kenta Abe,Minoru Matsui,Hisashi HayashiThrough the simulations carried out in the case studies, it is observed that both supervised neural networks NAR and NARX are viable methods for making predictions of multiple time series.

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Smart Innovation, Systems and TechnologiesIn relation to the case studies addressed throughout this work, we can point out that the importance of time series analysis is essential to have objective information on how we manage resources, time, and make decisions.

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Introduction to Prediction with Neural Networks,The human being can solve multiple problems simultaneously using different parts of the brain, which by itself represents a complex subject of study. For several decades, a great challenge has attracted the attention of the scientific community, causing different areas of study to join forces to understand how the human brain works.

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Literature Review on Prediction with Neural Networks,This chapter presents a theoretical summary of the methods used to design the proposed model, focusing on the general concepts of neural networks and fuzzy systems to address intelligent computing techniques as bioinspired methods. In addition, the topics of time series, cognitive flexibility, and decision are presented.

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Methodology for Prediction with Neural Networks,In general, our proposal consists of combining several methods to create a computational model that simulates the cognitive functioning of the human brain; focusing on the brain processes implicit in the mental routine that the human being performs to decision-making, where each mental process is directed towards the solution of a specific task.

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Results of Prediction with Neural Networks,For the case studies where the neural networks experiments were use, we performed 30 executions for each experiment.

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Discussion of Prediction Results with Neural Networks,Through the simulations carried out in the case studies, it is observed that both supervised neural networks NAR and NARX are viable methods for making predictions of multiple time series.

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