An Approximation to Deep Learning Touristic-Related Time Series Forecasting

Author
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 of Publication
2018
Date Published
11
Conference Location
Madrid (Spain)
ISBN Number
978-3-030-03493-1
DOI
10.1007/978-3-030-03493-1_47
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Number of Pages
448-456
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Notes

TIN2015-68854-R