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Advisor(s)
Abstract(s)
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.
Description
Keywords
Pedagogical Context
Citation
Teixeira, João Paulo; Fernandes, P. O. (2015). Tourism time series forecast. In Varajão, João eduardo [et al.] (eds.) Improving Organizational Effectiveness with Enterprise Information Systems. IGI Global. p. 72-87. ISBN 978-1-4666-8368-6
Publisher
IGI Global