PREDICTIVE TOOLS AS PART OF DECISSION AIDING PROCESSES AT THE AIRPORT – THE CASE OF FACEBOOK PROPHET LIBRARY
Sylwester KORGA
s.korga@pollub.plLublin 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 AnalysisReferences
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
Authors
Jerzy JÓZWIKLublin University of Technology Poland
Authors
Jarosław PYTKALublin University of Technology Poland
Authors
Kamil CYBULLublin University of Technology, Central Office of Measures Poland
Statistics
Abstract views: 273PDF downloads: 103
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Andrzej ŁUKASZEWICZ, Jerzy JÓZWIK, Kamil CYBUL, IMPACT OF FRICTION COEFFICIENT VARIATION ON TEMPERATURE FIELD IN ROTARY FRICTION WELDING OF METALS – FEM STUDY , Applied Computer Science: Vol. 19 No. 3 (2023)
- Jerzy JÓZWIK, Magdalena ZAWADA-MICHAŁOWSKA, Monika KULISZ, Paweł TOMIŁO, Marcin BARSZCZ, Paweł PIEŚKO, Michał LELEŃ, Kamil CYBUL, MODELING THE OPTIMAL MEASUREMENT TIME WITH A PROBE ON THE MACHINE TOOL USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Sylwester KORGA, Marcin BARSZCZ, Krzysztof DZIEDZIC, DEVELOPMENT OF SOFTWARE FOR IDENTIFICATION OF FILAMENTS USED IN 3D PRINTING TECHNOLOGY , Applied Computer Science: Vol. 15 No. 1 (2019)
- Kamil ŻYŁA, SIMPLIFIED GRAPHICAL DOMAIN-SPECIFIC LANGUAGES FOR THE MOBILE DOMAIN – PERSPECTIVES OF LEARNABILITY BY NONTECHNICAL USERS , Applied Computer Science: Vol. 13 No. 3 (2017)
Similar Articles
- Piotr WITTBRODT, Iwona ŁAPUŃKA, Gulzhan BAYTIKENOVA, Arkadiusz GOLA, Alfiya ZAKIMOVA, IDENTIFICATION OF THE IMPACT OF THE AVAILABILITY FACTOR ON THE EFFICIENCY OF PRODUCTION PROCESSES USING THE AHP AND FUZZY AHP METHODS , Applied Computer Science: Vol. 18 No. 4 (2022)
You may also start an advanced similarity search for this article.
