PREDVIĐANJE BROJA TURISTA PRIMENOM MAŠINSKOG UČENJA
Ključne reči:
mašinsko učenje, vremenske serije, turizam, posete, predviđanje
Sažetak
U ovom radu je dat pregled aktuelnih modela mašinskog učenja iz vremenskih serija i njihova primena za potrebe predviđanja broja turističkih poseta u narednom periodu. Pojava virusa Covid-19 je generalno imala veliki uticaj na Turizam i unela veliku neizvesnost u ovu oblast. Primena mašinskog učenja i pokušaj predviđanja broja turističkih poseta u narednom periodu, može da bude korisna onima koji se bave ponudom u ovoj oblasti.
Reference
Brownlee, J., 2018. Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. s.l.:Machine Learning Mastery.
Chollet F. et al., 2015. Keras. [Na mreži]
Available at: https://keras.io
[Poslednji pristup 2021].
Chollet, F., 2018. Deep Learning with Python. s.l.:Manning Publications Co..
Dozat, T., 2016. Incorporating Nesterov Momentum into Adam. s.l., ICLR 2016.
Fotiadis, A., Polyzos, S. & Huan, T.-C. T., 2021. The good, the bad and the ugly on COVID-19 tourism recovery. Annals of tourism research, Tom 87.
Ghalehkhondabi, I., Ardjmand, E., Young, W. A. & Weckman, G. R., 2019. A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of tourism futures, 5(1), pp. 75-99.
Graves, A., 2013. Generating Sequences With Recurrent Neural Networks. arXiv:1308.0850.
Huber , P. J., 1964. Robust Estimation of a Location Parameter,. The Annals of Mathematical Statistics, 35(1), pp. 73-101.
Republički zavod za statistiku, 2021. Dolasci i noćenja turista po regionima - mesečni podaci. [Na mreži]
Available at: https://data.stat.gov.rs/Home/Result/220203?languageCode=sr-Cyrl
[Poslednji pristup 04 2021].
Sun, S., Wei, Y., Tsui, K.-L. & Wang, S., 2019. Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, Tom 70, pp. 1-10.
TensorFlow, 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. [Na mreži]
Available at: http://tensorflow.org/
[Poslednji pristup 2021].
TensorFlow, 2021. Time series forecasting. [Na mreži]
Available at: https://www.tensorflow.org/tutorials/structured_data/time_series
[Poslednji pristup 2021].
Zaborovskaia, O., Sharafanova, E. & Maksanova, L., 2020. Scenario Forecasting Tourist Flows during the COVID-2019 Pandemic. International Journal of Technology, 11(8), pp. 1570-1578.
Zhang, H., Song, H., Wen, L. & Liu, C., 2021. Forecasting tourism recovery amid COVID-19. Annals of Tourism Research, 87(4).
Chollet F. et al., 2015. Keras. [Na mreži]
Available at: https://keras.io
[Poslednji pristup 2021].
Chollet, F., 2018. Deep Learning with Python. s.l.:Manning Publications Co..
Dozat, T., 2016. Incorporating Nesterov Momentum into Adam. s.l., ICLR 2016.
Fotiadis, A., Polyzos, S. & Huan, T.-C. T., 2021. The good, the bad and the ugly on COVID-19 tourism recovery. Annals of tourism research, Tom 87.
Ghalehkhondabi, I., Ardjmand, E., Young, W. A. & Weckman, G. R., 2019. A review of demand forecasting models and methodological developments within tourism and passenger transportation industry. Journal of tourism futures, 5(1), pp. 75-99.
Graves, A., 2013. Generating Sequences With Recurrent Neural Networks. arXiv:1308.0850.
Huber , P. J., 1964. Robust Estimation of a Location Parameter,. The Annals of Mathematical Statistics, 35(1), pp. 73-101.
Republički zavod za statistiku, 2021. Dolasci i noćenja turista po regionima - mesečni podaci. [Na mreži]
Available at: https://data.stat.gov.rs/Home/Result/220203?languageCode=sr-Cyrl
[Poslednji pristup 04 2021].
Sun, S., Wei, Y., Tsui, K.-L. & Wang, S., 2019. Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, Tom 70, pp. 1-10.
TensorFlow, 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. [Na mreži]
Available at: http://tensorflow.org/
[Poslednji pristup 2021].
TensorFlow, 2021. Time series forecasting. [Na mreži]
Available at: https://www.tensorflow.org/tutorials/structured_data/time_series
[Poslednji pristup 2021].
Zaborovskaia, O., Sharafanova, E. & Maksanova, L., 2020. Scenario Forecasting Tourist Flows during the COVID-2019 Pandemic. International Journal of Technology, 11(8), pp. 1570-1578.
Zhang, H., Song, H., Wen, L. & Liu, C., 2021. Forecasting tourism recovery amid COVID-19. Annals of Tourism Research, 87(4).
