Кузьменко, ДмитроВойтішин, Микита2025-09-042025-09-042025https://ekmair.ukma.edu.ua/handle/123456789/36428Over the recent years, financial time series modeling has presented a significant challenge due to market stochasticity and volatility. The stock market is influenced not only by market data such as price and volume but also by a wide range of additional external factors, including macroeconomic indicators, seasonality, fundamentals, and market sentiment. The increasing availability of diverse financial data, combined with the rapid advances in artificial intelligence (AI), has opened up new possibilities for analyzing and understanding how stock markets behave. These technologies have the potential to capture more complex nonlinear patterns that traditional statistical and machine learning models often fail to detect. This research examines how combining various model architectures and feature sets with domain - specific knowledge from the financial sector can enhance uncertainty quantification, a crucial aspect of making informed decisions and investments in financial markets.en-USfinancial time seriesmodelingstock marketartificial intelligence (AI)bachelor`s thesisOther