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  4. 126 — інформаційні системи та технології
Por favor, use este identificador para citar o enlazar este ítem: http://elartu.tntu.edu.ua/handle/lib/51448
Título : Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods
Autor : Valerie, Kasongo Bwanga
Affiliation: ТНТУ ім. І. Пулюя, Факультет комп’ютерно-інформаційних систем і програмної інженерії, Кафедра комп’ютерних наук, м. Тернопіль, Україна
Bibliographic description (Ukraine): Kasongo Bwanga V. Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods : Master’s qualification thesis in specialty 126 Information Systems and Technologies / supervisor O. Holotenko. — Ternopil : Ternopil Ivan Puluj National Technical University, 2026. — 97 p.
Bibliographic reference (2015): Kasongo Bwanga V. Machine Learning–Driven Weather Forecasting Integrating Advanced Data Analysis Methods: Master’s qualification thesis in specialty 126 Information Systems and Technologies / supervisor O. Holotenko. Ternopil: Ternopil Ivan Puluj National Technical University, 2026. 97 p.
Fecha de publicación : 26-ene-2026
Submitted date: 12-ene-2026
Date of entry: 28-ene-2026
Editorial : ТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Україна
Country (code): UA
Place of the edition/event: Тернопіль
Supervisor: Голотенко, Олександр Сергійович
Holotenko, Olexander
Committee members: Ясній, Олег Петрович
Yasniy, Oleg
UDC: 004.85:551.509
Palabras clave : 126
інформаційні системи та технології
аналіз даних
магістерська робота, машинне навчання, метеорологія, прогнозування погоди, часові ряди, data analysis, deep learning, machine learning, neural networks, time series forecasting, weather prediction
магістерська робота
машинне навчання
метеорологія
прогнозування погоди
часові ряди
data analysis
deep learning
machine learning
neural networks
time series forecasting
weather prediction
Page range: 97
Resumen : The qualification work is devoted to the development and research of machine learning models to improve the accuracy of short-term weather forecasting. The first chapter analyzes classical meteorological methods and modern approaches to processing large climate datasets. The second chapter focuses on the application of intelligent analysis methods, particularly time series analysis and dimensionality reduction techniques for input parameter preparation. The third chapter presents the results of training various neural network architectures, provides a comparative analysis of their effectiveness on real historical data, and estimates prediction errors. The findings demonstrate the high efficiency of machine learning methods in identifying complex non-linear patterns in meteorological data. The work also includes a section on occupational safety and the economic justification for the system's application
Descripción : Роботу виконано на кафедрі комп'ютерних наук Тернопільського національного технічного університету імені Івана Пулюя. Захист відбудеться 26.01.2026р. на засіданні екзаменаційної комісії №37 у Тернопільському національному технічному університеті імені Івана Пулюя
Content: INTRODUCTION 1 THEORETICAL FOUNDATIONS OF WEATHER FORECASTING 1.1 Overview of traditional numerical weather prediction 1.2 Role of big data and AI in meteorology 1.3 Review of advanced data analysis techniques 2 METHODOLOGY AND DATA PREPROCESSING 2.1 Data sources and feature engineering for climate variables 2.2 Dimensionality reduction and time series decomposition 2.3 Selection of machine learning algorithms 3 MODEL IMPLEMENTATION AND COMPARATIVE ANALYSIS 3.1 Development of predictive models (SARIMA, Random Forest, Neural Networks) 3.2 Training, validation and error assessment 3.3 Discussion of results and accuracy improvements 4 ECONOMIC JUSTIFICATION OF THE PROPOSED SOLUTION 5 OCCUPATIONAL HEALTH AND SAFETY IN EMERGENCY SITUATIONS CONCLUSIONS REFERENCES
URI : http://elartu.tntu.edu.ua/handle/lib/51448
Copyright owner: © Valerie Kasongo Bwanga, 2026
References (Ukraine): 1. Electricity price statistics. Statistics Explained. 2024. P. 2–3. URL: https://ec.europa.eu/eurostat/statistics-explained/SEPDF/cache/45239.pdf (date of access: 01.10.2024).
2. Impact of forecasting on energy system optimization / F. Peterssen et al. Advances in Applied Energy. 2024. Vol. 15. 100181. URL: https://doi.org/10.1016/j.adapen.2024.100181 (date of access: 22.10.2024).
3. Kolambe M., Arora S. Forecasting the Future: A Comprehensive Review of Time Series Prediction Techniques. Journal of Electrical Systems. 2024. Vol. 20, no. 2s. P. 575–586. URL: https://doi.org/10.52783/jes.1478 (date of access: 22.10.2024).
4. Shumway R. H., Stoffer D. S. ARIMA Models. Time Series: A Data Analysis Approach Using R. Boca Raton: CRC Press, Taylor & Francis Group, 2019. P. 99–128. URL: https://doi.org/10.1201/9780429273285-5 (date of access: 22.10.2024).
5. Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting / S. I. Vagropoulos et al. 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium, 4–8 April 2016. 2016. URL: https://doi.org/10.1109/energycon.2016.7514029 (date of access: 22.10.2024).
6. Maulud D., Abdulazeez A. M. A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends. 2020. Vol. 1, no. 4. P. 140–147. URL: https://doi.org/10.38094/jastt1457 (date of access: 22.10.2024).
7. Box G. E. P., Jenkins G. M., Reinsel G. C., Ljung G. M. Time Series Analysis: Forecasting and Control. 5th ed. Hoboken: John Wiley & Sons, 2015. 712 p.
8. Hyndman R. J., Athanasopoulos G. Forecasting: Principles and Practice. 3rd ed. OTexts, 2021. URL: https://otexts.com/fpp3/ (date of access: 22.10.2024).
9. Brockwell P. J., Davis R. A. Introduction to Time Series and Forecasting. 3rd ed. Springer, 2016. 425 p.
10. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, 2009. 745 p.
11. Hamilton J. D. Time Series Analysis. Princeton University Press, 1994. 799 p.
12. Box G. E. P., Jenkins G. M. Some Recent Advances in Forecasting and Control. Applied Statistics. 1970. Vol. 19, no. 1. P. 91–109.
13. De Gooijer J. G., Hyndman R. J. 25 Years of Time Series Forecasting. International Journal of Forecasting. 2006. Vol. 22, no. 3. P. 443–473.
14. Makridakis S., Wheelwright S. C., Hyndman R. J. Forecasting: Methods and Applications. 3rd ed. John Wiley & Sons, 1998. 656 p.
15. Bishop C. M. Pattern Recognition and Machine Learning. Springer, 2006. 738 p.
16. Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, 2016. 775 p.
17. Montgomery D. C., Jennings C. L., Kulahci M. Introduction to Time Series Analysis and Forecasting. 2nd ed. Wiley, 2015. 472 p.
18. Tibshirani R., Friedman J. H., Hastie T. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010. Vol. 33, no. 1. P. 1–22.
19. Tsay R. S. Analysis of Financial Time Series. 3rd ed. Wiley, 2010. 720 p.
20. Wei W. W. S. Time Series Analysis: Univariate and Multivariate Methods. 2nd ed. Addison-Wesley, 2006. 618 p.
21. Li M., Fu G., Zhang Y. Short-Term Wind Speed Forecasting Using SARIMA Model. Renewable Energy. 2022. Vol. 187. P. 591–600.
22. Engle R. F., Granger C. W. J. Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica. 1987. Vol. 55, no. 2. P. 251–276.
23. Jolliffe I. T. Principal Component Analysis. 2nd ed. Springer, 2002. 487 p.
24. Walker G. T. On Periodicity in Series of Related Terms. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 1931. Vol. 131, no. 818. P. 518–532.
25. Box G. E. P., Pierce D. A. Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models. Journal of the American Statistical Association. 1970. Vol. 65, no. 332. P. 1509–1526.
26. Brockwell P. J., Davis R. A. Time Series: Theory and Methods. 2nd ed. Springer, 1991. 577 p.
27. Gelman A., Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006. 625 p.
28. Cressie N. Statistics for Spatial Data. Revised ed. Wiley, 1993. 900 p.
29. Fuller W. A. Introduction to Statistical Time Series. 2nd ed. Wiley, 1996. 698 p.
30. Enders W. Applied Econometric Time Series. 4th ed. Wiley, 2014. 496 p.
31. Box G. E. P., Tiao G. C. Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association. 1975. Vol. 70, no. 349. P. 70–79.
Content type: Master Thesis
Aparece en las colecciones: 126 — інформаційні системи та технології

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