02615ntm a22005057i 4500 000703477 CZ-PrVSE 20240203150629.0 m d cr n|||||||||| 240203s2023 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Zheng, Yue ISIS:165345 dis Time Series Analysis of Stock Index Forecasting eng Time Series Analysis of Stock Index Forecasting / Yue Zheng 2023 ?? stran : digital, PDF soubor Vedoucí práce: Ondřej Šimpach Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 This thesis investigates stock market forecasting by comparing traditional time series models (ARIMA-GARCH), deep learning models (LSTM), and a hybrid ARIMA-LSTM model, focusing on the S&P 500, Euro Stoxx 50, and CSI 300 indices. Accuracy is evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), with findings indicating varying model effectiveness based on specific market data characteristics. The ARIMA-LSTM hybrid model excels in markets with strong autocorrelation, such as the S&P 500, demonstrating its ability to capture both linear and non-linear dynamics. In contrast, the single LSTM model is more effective in markets like the Euro Stoxx 50 and CSI 300, where weaker autocorrelation is present. Způsob přístupu: Internet data analysis and modeling [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas ARIMA LSTM stock index time series Šimpach, Ondřej jo2013796555 ths Helman, Karel xx0260953 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/82621/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/82621 Hlavní práce https://insis.vse.cz/zp/82621/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/82621/posudek/oponent/80716 Oponentura https://insis.vse.cz/zp/82621/podrobnosti dc:identifier NEPOSILAT VSKP vse82621 240130 82621