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Designing a Neural Network Model for Time Series Forecasting

Designing a Neural Network Model for Time Series Forecasting
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Author(s): Paola Andrea Sanchéz Sanchéz (Universidad Simon Bolivar, Colombia), José Rafael García González (Universidad Simon Bolivar, Colombia), Carlos Hernán Fajardo-Toro (Universidad EAN, Colombia)and Paloma María Teresa Martínez Sánchez (Universidad El Bosque, Colombia)
Copyright: 2020
Pages: 26
Source title: Theoretical and Applied Mathematics in International Business
Source Author(s)/Editor(s): Bryan Christiansen (Global Research Society, USA)and Fatima Shuwaikh (Université Paris Saclay, France)
DOI: 10.4018/978-1-5225-8458-2.ch012

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Abstract

Artificial neural networks are highly flexible and efficient tools in the approximation of time series patterns. In recent years, more than 5,000 studies oriented to the use of neural networks in time series forecasting have been evidenced in the extant literature. However, the methodology used for its specification and construction still involves a lot of trial and error or is inherited from econometric and statistical procedures that do not fit perfectly to the characteristics of the time series. This is especially true when they present non-linear behavior; moreover, it is not designed for working with neural networks. The objective of this chapter is to present a five-step guide for the specification, design, and validation of a neural network model for forecasting time series.

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