Time Series Forecasting Using Machine Learning
| but.committee | prof. Ing. Jan Hajný, Ph.D. (předseda) doc. Ing. Jiří Hošek, Ph.D. (místopředseda) Ing. Josef Vojtěch, Ph.D. (člen) Ing. Jan Látal, Ph.D. (člen) Ing. Martin Štůsek, Ph.D. (člen) doc. Ing. Petr Münster, Ph.D. (člen) | cs |
| but.defence | Student presented the results of his thesis and the committee got familiar with reviewer's report. Opponent's question: - What are your thought about using some of the famous large language models (LLMs) such as models from the GPT-x family for time series prediction? - The student answered the question sufficiently. - How would you tackle data with many missing values? - The student answered the question sufficiently. Student failed to defend his Diploma Thesis. | cs |
| but.jazyk | angličtina (English) | |
| but.program | Communications and Networking | cs |
| but.result | práce nebyla úspěšně obhájena | cs |
| dc.contributor.advisor | Hošek, Jiří | en |
| dc.contributor.author | Elrefaei, Islam Riad | en |
| dc.contributor.referee | Galáž, Zoltán | en |
| dc.date.created | 2024 | cs |
| dc.description.abstract | The aim of this thesis is to explore the application of various artificial intelligence (AI) techniques for the prediction of time series data, which is prevalent in fields such as finance, economics, and engineering. Accurate time series prediction is essential for effective decision-making and planning. This thesis reviews several traditional and state-of-the-art AI techniques used for time series prediction, including linear regression, ARIMA, support vector regression, random forests, and deep learning. These techniques are applied to different time series datasets, encompassing both univariate and multivariate data. The performance of the predictive models is evaluated using various scalar metrics. The performance of the models was different depending on the type of the dataset. Additionally, this thesis includes the development of a user interface application that allows users to change parameters and forecast new results based on their entries. Furthermore, the thesis discusses the challenges and limitations of using AI techniques for time series prediction and provides suggestions for future research directions. | en |
| dc.description.abstract | The aim of this thesis is to explore the application of various artificial intelligence (AI) techniques for the prediction of time series data, which is prevalent in fields such as finance, economics, and engineering. Accurate time series prediction is essential for effective decision-making and planning. This thesis reviews several traditional and state-of-the-art AI techniques used for time series prediction, including linear regression, ARIMA, support vector regression, random forests, and deep learning. These techniques are applied to different time series datasets, encompassing both univariate and multivariate data. The performance of the predictive models is evaluated using various scalar metrics. The performance of the models was different depending on the type of the dataset. Additionally, this thesis includes the development of a user interface application that allows users to change parameters and forecast new results based on their entries. Furthermore, the thesis discusses the challenges and limitations of using AI techniques for time series prediction and provides suggestions for future research directions. | cs |
| dc.description.mark | E | cs |
| dc.identifier.citation | ELREFAEI, I. Time Series Forecasting Using Machine Learning [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2024. | cs |
| dc.identifier.other | 153602 | cs |
| dc.identifier.uri | http://hdl.handle.net/11012/246057 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights | Standardní licenční smlouva - přístup k plnému textu bez omezení | cs |
| dc.subject | Time Series | en |
| dc.subject | Forecasting | en |
| dc.subject | Python | en |
| dc.subject | Machine Learning | en |
| dc.subject | Time Series | cs |
| dc.subject | Forecasting | cs |
| dc.subject | Python | cs |
| dc.subject | Machine Learning | cs |
| dc.title | Time Series Forecasting Using Machine Learning | en |
| dc.title.alternative | Time Series Forecasting Using Machine Learning | cs |
| dc.type | Text | cs |
| dc.type.driver | masterThesis | en |
| dc.type.evskp | diplomová práce | cs |
| dcterms.dateAccepted | 2024-06-06 | cs |
| dcterms.modified | 2024-08-29-12:19:05 | cs |
| eprints.affiliatedInstitution.faculty | Fakulta elektrotechniky a komunikačních technologií | cs |
| sync.item.dbid | 153602 | en |
| sync.item.dbtype | ZP | en |
| sync.item.insts | 2025.03.26 14:41:54 | en |
| sync.item.modts | 2025.01.17 09:35:49 | en |
| thesis.discipline | bez specializace | cs |
| thesis.grantor | Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikací | cs |
| thesis.level | Inženýrský | cs |
| thesis.name | Ing. | cs |