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Tourism Time Series Forecast

Tourism Time Series Forecast

João Paulo Teixeira, Paula Odete Fernandes
Copyright: © 2015 |Pages: 16
ISBN13: 9781466683686|ISBN10: 1466683686|EISBN13: 9781466683693
DOI: 10.4018/978-1-4666-8368-6.ch005
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MLA

Teixeira, João Paulo, and Paula Odete Fernandes. "Tourism Time Series Forecast." Improving Organizational Effectiveness with Enterprise Information Systems, edited by João Eduardo Varajão, et al., IGI Global, 2015, pp. 72-87. https://doi.org/10.4018/978-1-4666-8368-6.ch005

APA

Teixeira, J. P. & Fernandes, P. O. (2015). Tourism Time Series Forecast. In J. Varajão, M. Cruz-Cunha, & R. Martinho (Eds.), Improving Organizational Effectiveness with Enterprise Information Systems (pp. 72-87). IGI Global. https://doi.org/10.4018/978-1-4666-8368-6.ch005

Chicago

Teixeira, João Paulo, and Paula Odete Fernandes. "Tourism Time Series Forecast." In Improving Organizational Effectiveness with Enterprise Information Systems, edited by João Eduardo Varajão, Maria Manuela Cruz-Cunha, and Ricardo Martinho, 72-87. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8368-6.ch005

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Abstract

In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The index time features associated to the previous twelve values of the time series proved its relevance in this forecast task. The insolation variable can improved results with some architectures, namely the cascade forward architecture. Finally, the experimented ANN models/architectures produced a mean absolute percentage error between 4 and 6%, proving the ability of the ANN models based to forecast this time series. Besides, the feedforward architecture behaved better considering validation and test sets, with 4.2% percentage error in test set.

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