Designing a Neural Network Model for Time Series Forecasting

Designing a Neural Network Model for Time Series Forecasting

Paola Andrea Sanchéz Sanchéz, José Rafael García González, Carlos Hernán Fajardo-Toro, Paloma María Teresa Martínez Sánchez
Copyright: © 2020 |Pages: 26
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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Methodology Proposed For The Construction Of Neural Network Models

The strategy proposed in this work is based on a systemic approach oriented to the specification of neural network models for the prediction of time series, highlighting the importance of considering key aspects that have been ignored in the theory and that lead to strong implications in its application, such as the optimization algorithm and the techniques for selecting the best model. Starting from the existence of a time series that is to be modeled and forecasted, the proposed steps for the specification and construction of the neural network model are presented in Figure 1.

Figure 1.

Diagram of proposed steps for the specification and construction of the neural network model

978-1-5225-8458-2.ch012.f01
Source. Elabored by the authors

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