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 |
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