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An Approximation to Deep Learning Touristic-Related Time Series Forecasting

  • Daniel Trujillo ViedmaEmail author
  • Antonio Jesús Rivera Rivas
  • Francisco Charte Ojeda
  • María José del Jesus Díaz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11314)

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.

Keywords

LSTM ARIMA Time series forecasting 

Notes

Acknowledgements

This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Daniel Trujillo Viedma
    • 1
    Email author
  • Antonio Jesús Rivera Rivas
    • 1
  • Francisco Charte Ojeda
    • 1
  • María José del Jesus Díaz
    • 1
  1. 1.Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), Computer Science DepartmentUniversity of JaénJaénSpain

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