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Time Series Forecasting Using Machine Learning
Elrefaei, Islam ; Galáž, Zoltán (referee) ; Hošek, Jiří (advisor)
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.
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