Time Series Forecasting Using Machine Learning
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Date
Authors
Elrefaei, Islam Riad
ORCID
Advisor
Referee
Mark
E
Journal Title
Journal ISSN
Volume Title
Publisher
Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
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.
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.
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.
Description
Citation
ELREFAEI, I. Time Series Forecasting Using Machine Learning [online]. Brno: Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. 2024.
Document type
Document version
Date of access to the full text
Language of document
en
Study field
bez specializace
Comittee
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)
Date of acceptance
2024-06-06
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.
Result of defence
práce nebyla úspěšně obhájena
Document licence
Standardní licenční smlouva - přístup k plnému textu bez omezení