|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|
|Conference Location||Madrid (Spain)|
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.