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
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
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
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
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
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
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
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
IMGW. (2022). Homepage of the Institute of Meteorology and Water Management - National Research Institute. https://www.imgw.pl
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
Keras. (2023). Keras library homepage. https://keras.io
Krieger, M. (2021, February 20). Time series analysis with Facebook Prophet: How it works and how to use it. https://towardsdatascience.com
Mitchell, T. M. (1997). Machine learning. McGraw-Hill Science.
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
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
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
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
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
Prophet. (2022). Seasonality, holiday effects, and regressors. https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html
Prophet. (2023). Facebook Prophet library homepage. https://facebook.github.io/prophet/
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
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
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
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
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
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
TensorFlow. (2023). TensorFlow library homepage. https://www.tensorflow.org
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
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
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
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