PREDICTIVE TOOLS AS PART OF DECISSION AIDING PROCESSES AT THE AIRPORT – THE CASE OF FACEBOOK PROPHET LIBRARY

Sylwester KORGA

s.korga@pollub.pl
Lublin University of Technology (Poland)

Kamil ŻYŁA


Lublin University of Technology (Poland)
https://orcid.org/0000-0002-6291-003X

Jerzy JÓZWIK


Lublin University of Technology (Poland)

Jarosław PYTKA


Lublin University of Technology (Poland)

Kamil CYBUL


Lublin University of Technology, Central Office of Measures (Poland)

Abstract

Prophet is a quite fresh and promising open-source library for machine learning, developed by Facebook, that gains some significant interest. It could be used for predicting time series taking into account holidays and seasonality effects. Its possible applications and deficit of scientific works concerning its usage within decision processes convinced the authors to state the research question, if the Prophet library could provide reliable prediction to support decision-making processes at the airport. The case of Radawiec airport (located near Lublin, Poland) was chosen. Official measurement data (from the last 4 years) published by the Polish Government  Institute was used to train the neural network and predict daily averages of wind speed, temperature, pressure, relative humidity and rainfall totals during the day and night. It was revealed that most of the predicted data points were within the acceptance threshold, and computations were fast and highly automated. However, the authors believe that the Prophet library is not particularly useful for airport decision-making processes because the way it handles additional regressors and susceptibility to unexpected phenomena negatively affects the reliability of prediction results.


Keywords:

weather prediction, Facebook Prophet, aviation, machine learning, Data Analysis

Akula, R., Wieselthier, Z., Martin, L., & Garibay, I. (2019). Forecasting the success of television series using machine learning. ArXiv, abs/1910.12589. https://doi.org/10.48550/arXiv.1910.12589
DOI: https://doi.org/10.1109/SoutheastCon42311.2019.9020419   Google Scholar

Asha, J., Rishidas, S., SanthoshKumar, S., & Reena, P. (2020). Analysis of temperature prediction using random forest and Facebook Prophet algorithms. Lecture Notes on Data Engineering and Communications Technologies, 46, 432-439. https://doi.org/10.1007/978-3-030-38040-3_49
DOI: https://doi.org/10.1007/978-3-030-38040-3_49   Google Scholar

Banga, A., Ahuja, R., & Sharma, S. C. (2021). Stacking machine learning models to forecast hourly and daily electricity consumption of household using Internet of Things. Journal Of Scientific & Industrial Research, 80, 894-904.
DOI: https://doi.org/10.56042/jsir.v80i10.42241   Google Scholar

Bendiek, P., Taha, A., Abbasi, Q. H., & Barakat, B. (2022). Solar irradiance forecasting using a data-driven algorithm and contextual optimisation. Applied Sciences, 12(1), 134. https://doi.org/10.3390/app12010134
DOI: https://doi.org/10.3390/app12010134   Google Scholar

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., … Vitart, F. (2011). The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828
DOI: https://doi.org/10.1002/qj.828   Google Scholar

El Hachimi, C., Belaqziz, S., Khabba, S., & Chehbouni, A. (2021). Towards precision agriculture in Morocco: A machine learning approach for recommending crops and forecasting weather. 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA) (pp. 88–95). IEEE. https://doi.org/10.1109/ICDATA52997.2021.00026
DOI: https://doi.org/10.1109/ICDATA52997.2021.00026   Google Scholar

Garlapati, A., Krishna, D. R., Garlapati, K., Srikara Yaswanth, N. M., Rahul, U., & Narayanan, G. (2021). Stock Price Prediction Using Facebook Prophet and Arima Models. 2021 6th International Conference for Convergence in Technology (I2CT) (pp. 1–7). IEEE. https://doi.org/10.1109/I2CT51068.2021.9418057
DOI: https://doi.org/10.1109/I2CT51068.2021.9418057   Google Scholar

Haq, M. A. (2022). CDLSTM: A novel model for climate change forecasting. Computers Materials & Continua, 71(2), 2363-2381. https://doi.org/10.32604/cmc.2022.023059
DOI: https://doi.org/10.32604/cmc.2022.023059   Google Scholar

IMGW. (2022). Homepage of the Institute of Meteorology and Water Management - National Research Institute. https://www.imgw.pl
  Google Scholar

Junsuk, K., & Tae, J. K. (2021). Application of Facebook’s Prophet model for forecasting meteorological data. Journal of the Korean Society of Hazard Mitigation, 21(2), 53-58. https://doi.org/10.9798/KOSHAM.2021.21.2.53
DOI: https://doi.org/10.9798/KOSHAM.2021.21.2.53   Google Scholar

Keras. (2023). Keras library homepage. https://keras.io
  Google Scholar

Krieger, M. (2021, February 20). Time series analysis with Facebook Prophet: How it works and how to use it. https://towardsdatascience.com
  Google Scholar

Mitchell, T. M. (1997). Machine learning. McGraw-Hill Science.
  Google Scholar

Narmeen, M., Sattar, M. U., Khan, H. W., Fatima, M., Azad, M.-D., & Ghani, F. (2022). Impact of Weather on COVID-19 in Metropolitan Cities ofPakistan: A Data-Driven Approach. International Journal of Computing and Digital Systems, 11(1), 905–915. https://doi.org/10.12785/ijcds/110174
DOI: https://doi.org/10.12785/ijcds/110174   Google Scholar

Oo, Z. Z., & Phyu, S. (2019). Microclimate Prediction Using Cloud Centric Model Based on IoT Technology for Sustainable Agriculture. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) (pp. 660–663). IEEE. https://doi.org/10.1109/CCOMS.2019.8821705
DOI: https://doi.org/10.1109/CCOMS.2019.8821705   Google Scholar

Ortiz-Bejar, J., Ortiz-Bejar, J., Zamora-Mendez, A., Pineda-Garcia, G., Graff, M., & Tellez, E. S. (2020). Forecasting without context problem. 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (pp. 1–6). IEEE. https://doi.org/10.1109/ROPEC50909.2020.9258744
DOI: https://doi.org/10.1109/ROPEC50909.2020.9258744   Google Scholar

Papacharalampous, G., Tyralis, H., & Koutsoyiannis, D. (2018). Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophysica, 66, 807-831. https://doi.org/10.1007/s11600-018-0120-7
DOI: https://doi.org/10.1007/s11600-018-0120-7   Google Scholar

Patil, S., & Pandya, S. (2021). Forecasting dengue hotspots associated with variation in meteorological parameters using regression and time series models. Frontiers in Public Health, 9, 798034. https://doi.org/10.3389/fpubh.2021.798034
DOI: https://doi.org/10.3389/fpubh.2021.798034   Google Scholar

Prophet. (2022). Seasonality, holiday effects, and regressors. https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html
  Google Scholar

Prophet. (2023). Facebook Prophet library homepage. https://facebook.github.io/prophet/
  Google Scholar

Qiu, Q., He, J., Chen, H., & Qiu, J. (2019). Research on the evolution law of emergency network public opinion. 2019 12th International Symposium on Computational Intelligence and Design (ISCID) ( pp. 157–161). IEEE. https://doi.org/10.1109/ISCID.2019.10119
DOI: https://doi.org/10.1109/ISCID.2019.10119   Google Scholar

Ryu, S., Nam, H. J., Kim, J. M., & Kim, S. W. (2021). Current and future trends in hospital utilization of patients with schizophrenia in Korea: A time series analysis using national health insurance data. Psychiatry Investigation, 18(8), 795-800. https://doi.org/10.30773/pi.2021.0071
DOI: https://doi.org/10.30773/pi.2021.0071   Google Scholar

Shenbagalakshmi, V., & Jaya, T. (2022). Application of machine learning and IoT to enable child safety at home environment. Journal of Supercomputing, 78, 10357–10384. https://doi.org/10.1007/s11227-022-04310-z
DOI: https://doi.org/10.1007/s11227-022-04310-z   Google Scholar

Soltaganov, N. A., Sherstnev, V. S., Sherstneva, A. I., Botygin, I. A., & Krutikov, V. A. (2018). Construction of predictive models of meteorological parameters of the atmospheric surface layer. IOP Conference Series: Earth and Environmental Science, 211, 012027. https://doi.org/10.1088/1755-1315/211/1/012027
DOI: https://doi.org/10.1088/1755-1315/211/1/012027   Google Scholar

Sulasikin, A., Nugraha, Y., Kanggrawan, J. I., & Suherman, A. L. (2021). Monthly rainfall prediction using the Facebook Prophet model for flood mitigation in central Jakarta. 2021 International Conference on ICT for Smart Society (ICISS) (pp. 1-5). IEEE. https://doi.org/10.1109/ICISS53185.2021.9532507
DOI: https://doi.org/10.1109/ICISS53185.2021.9532507   Google Scholar

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37-45. https://doi.org/10.1080/00031305.2017.1380080
DOI: https://doi.org/10.1080/00031305.2017.1380080   Google Scholar

TensorFlow. (2023). TensorFlow library homepage. https://www.tensorflow.org
  Google Scholar

Thiyagarajan, K., Kodagoda, S., Ulapane, N., & Prasad, M. (2020). A temporal forecasting driven approach ysing facebook’s prophet method for anomaly detection in sewer air temperature sensor system. 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 25–30). IEEE. https://doi.org/10.1109/ICIEA48937.2020.9248142
DOI: https://doi.org/10.1109/ICIEA48937.2020.9248142   Google Scholar

Toharudin, T., Pontoh, R. S., Caraka, R. E., Zahroh, S., Lee, Y., & Chen, R. C. (2021). Employing long short-term memory and Facebook prophet model in air temperature forecasting. Communications in Statistics-Simulation and Computation, 52(2), 279-290. https://doi.org/10.1080/03610918.2020.1854302
DOI: https://doi.org/10.1080/03610918.2020.1854302   Google Scholar

Urząd Lotnictwa Cywilnego. (2022). Meteorological service for international air navigation. Annex 3 to the Convention on International Civil Aviation. https://www.ulc.gov.pl/_download/prawo/prawo_miedzynarodowe/konwencje/Zal%C4%85cznik_3_cz_I_cz_II.pdf
  Google Scholar

Weytjens, H., Lohmann, E., & Kleinsteuber, M. (2021). Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet. Electronic Commerce Research, 21, 371-391. https://doi.org/10.1007/s10660-019-09362-7
DOI: https://doi.org/10.1007/s10660-019-09362-7   Google Scholar

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Published
2023-12-31

Cited by

KORGA, S., ŻYŁA, K., JÓZWIK, J., PYTKA, J., & CYBUL, K. (2023). PREDICTIVE TOOLS AS PART OF DECISSION AIDING PROCESSES AT THE AIRPORT – THE CASE OF FACEBOOK PROPHET LIBRARY. Applied Computer Science, 19(4), 51–67. https://doi.org/10.35784/acs-2023-35

Authors

Sylwester KORGA 
s.korga@pollub.pl
Lublin University of Technology Poland

Authors

Kamil ŻYŁA 

Lublin University of Technology Poland
https://orcid.org/0000-0002-6291-003X

Authors

Jerzy JÓZWIK 

Lublin University of Technology Poland

Authors

Jarosław PYTKA 

Lublin University of Technology Poland

Authors

Kamil CYBUL 

Lublin University of Technology, Central Office of Measures Poland

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