| Title | An Approximation to Deep Learning Touristic-Related Time Series Forecasting |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Trujillo, Daniel, Rivera-Rivas A.J., Charte Francisco, and del Jesus M. J. |
| Conference Name | 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 |
| Pagination | 448–456 |
| Date Published | 11 |
| Conference Location | Madrid (Spain) |
| ISBN Number | 978-3-030-03493-1 |
| 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. |
| Notes | TIN2015-68854-R |
| DOI | 10.1007/978-3-030-03493-1_47 |
Fichero:
