03163ntm a22004937i 4500 000722909 CZ-PrVSE 20250608110309.0 m d cr n|||||||||| 250608s2025 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Simon, Jan ISIS:125981 dis Předpovídání makrofinančních časových řad pomocí strojového učení eng Forecasting Macrofinancial Time Series with Machine Learning Methods / Jan Simon 2025 ?? stran : digital, PDF soubor Vedoucí práce: Miroslav Plašil Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2025 Unlike the standard supervised learning setting, forecasting time series poses different challenges due to their inherent time dependence, non-stationarity, and susceptibility to structural shifts. Traditional econometric models, while powerful, might struggle capturing the nonlinear patterns and high-dimensional dependencies present in such data. In recent years, machine learning (ML) methods have emerged as promising alternatives, offering flexible approaches to modeling and prediction. In this thesis, we (1) build the foundational knowledge related to timeseries as well as practical applications , (2) introduce the basic building blocks of both classical and ML algorithms and finally (3) compare these models in multiple different settings - low to high amount of training data with low to high forecasting horizons. Finally, all tests are conducted on 3 different real world datasets to allow us to generalize our findings. We find that ensemble methods perform better across majority of settings, with the exception of low signal-to-noise datasets, where simpler methods perform better. Additionally, regularized models tend to perform at least as good as its non-regularized counterparts, though at the cost of computation. Způsob přístupu: Internet statistika [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Time series Machine learning Probability theory Plašil, Miroslav ola2012709459 ths Karel, Tomáš vse2015858033 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/82528/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/82528 Hlavní práce https://insis.vse.cz/zp/82528/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/82528/posudek/oponent/86641 Oponentura https://insis.vse.cz/zp/82528/podrobnosti dc:identifier NEPOSILAT VSKP vse82528 250604 82528