@conference {306,
	title = {An Approximation to Deep Learning Touristic-Related Time Series Forecasting},
	booktitle = {19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018},
	year = {2018},
	note = {TIN2015-68854-R},
	month = {11},
	pages = {448{\textendash}456},
	address = {Madrid (Spain)},
	abstract = {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.},
	isbn = {978-3-030-03493-1},
	doi = {10.1007/978-3-030-03493-1_47},
	author = {Daniel Trujillo and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
