An Approximation to Deep Learning Touristic-Related Time Series Forecasting

TitleAn Approximation to Deep Learning Touristic-Related Time Series Forecasting
Publication TypeConference Paper
Year of Publication2018
AuthorsTrujillo, Daniel, Rivera Antonio J., Charte Francisco, and del Jesus M. J.
Conference Name19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Pagination448–456
Date Published11
Conference LocationMadrid (Spain)
ISBN Number978-3-030-03493-1
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

DOI10.1007/978-3-030-03493-1_47