@inproceedings{426,
  author = {Daniel Trujillo and Antonio Jesús Rivera Rivas and Francisco Charte Ojeda and Maria José del Jesus Díaz},
  title = {An Approximation to Deep Learning Touristic-Related Time Series Forecasting},
  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.},
  year = {2018},
  pages = {448-456},
  month = {11},
  address = {Madrid (Spain)},
  isbn = {978-3-030-03493-1},
  doi = {10.1007/978-3-030-03493-1_47},
  note = {TIN2015-68854-R},
}