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An Approximation to Deep Learning Touristic-Related Time Series Forecasting
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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.
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Year of Publication |
2018
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Date Published |
11
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Conference Location |
Madrid (Spain)
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ISBN Number |
978-3-030-03493-1
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DOI |
10.1007/978-3-030-03493-1_47
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Download citation | |
Number of Pages |
448-456
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Bibliography media |
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Notes |
TIN2015-68854-R |