Time Series Forecasting Using Machine Learning
| but.committee | Ing. Karel Kuchař (člen) Ing. Radim Číž, Ph.D. (člen) doc. Ing. Tomáš Horváth, Ph.D. (člen) Ing. Lukáš Benešl (člen) prof. Ing. Dan Komosný, Ph.D. (předseda) doc. Ing. Jiří Hošek, Ph.D. (místopředseda) | cs |
| but.defence | Student presented the results of his thesis and the committee got familiar with reviewer's report. Student defended his Diploma Thesis with reservations and answered the questions from the members of the committee and the reviewer. Questions: How would you test the results for the statistical significance? How would you increase the generalization power of the trained models? What would be you next steps if I ask you to use the best performing model and put it to action on a test usecase exposed to users? Questions of committee: How many data did you use for machine learning? How can you measure the errors of datasets? How long forward you can predict? | cs |
| but.jazyk | angličtina (English) | |
| but.program | Communications and Networking | cs |
| but.result | práce byla 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 | 161929 | cs |
| dc.identifier.uri | http://hdl.handle.net/11012/249586 | |
| 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 | Prediction | en |
| dc.subject | Machine Learning | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Deep Learning | en |
| dc.subject | Time Series | cs |
| dc.subject | Forecasting | cs |
| dc.subject | Prediction | cs |
| dc.subject | Machine Learning | cs |
| dc.subject | Artificial Intelligence | cs |
| dc.subject | Deep 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 | 2024-08-27 | cs |
| dcterms.modified | 2024-08-29-12:19:05 | cs |
| eprints.affiliatedInstitution.faculty | Fakulta elektrotechniky a komunikačních technologií | cs |
| sync.item.dbid | 161929 | en |
| sync.item.dbtype | ZP | en |
| sync.item.insts | 2025.03.26 14:42:31 | en |
| sync.item.modts | 2025.01.15 23:35:48 | 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 |
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