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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References
Altés, C.: Marketing y turismo. Editorial Síntesis, Madrid (1993)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Cuadrado Roura, J.R., López Morales, J.M., et al.: El turismo, motor del crecimiento y de la recuperación de la economía española (2015)
de Estadística, I.N.: Aportación del turismo a la economía española (2016). http://www.ine.es/
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)
Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., et al.: Gradient flow in recurrent nets: the difficulty of learning long-term dependencies (2001)
Hyndman, R.J., Khandakar, Y., et al.: Automatic time series for forecasting: the forecast package for R. No. 6/07. Monash University, Department of Econometrics and Business Statistics (2007)
Romanjuk, V.V.: Training data expansion and boosting of convolutional neural networks for reducing the mnist dataset error rate. Naukovi Visti NTUU KPI 6, 29–34 (2016)
Schmidhuber, J., Hochreiter, S.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Werbos, P.J.: Generalization of backpropagation with application to a recurrent gas market model. Neural Netw. 1(4), 339–356 (1988)
Acknowledgements
This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.
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Viedma, D.T., Rivas, A.J.R., Ojeda, F.C., del Jesus Díaz, M.J. (2018). An Approximation to Deep Learning Touristic-Related Time Series Forecasting. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_47
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DOI: https://doi.org/10.1007/978-3-030-03493-1_47
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