Tourism is one of the biggest economic activities around the world. This means that an adequate planning of existing resources becomes crucial. Precise demand-related forecasting greatly improves this planning. Deep Learning models are showing an greatly improvement on time-series forecasting, particularly the LSTM, which is designed for this kind of tasks. This article introduces the touristic time-series forecasting using LSTM, and compares its accuracy against well known models RandomForest and ARIMA. Our results shows that new LSTM models achieve the best accuracy.
CITATION STYLE
Viedma, D. T., Rivas, A. J. R., Ojeda, F. C., & del Jesus Díaz, M. J. (2018). An Approximation to Deep Learning Touristic-Related Time Series Forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11314 LNCS, pp. 448–456). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_47
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