03268ntm a22005417i 4500 000716595 CZ-PrVSE 20240824093620.0 m d cr n|||||||||| 240824s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Billinton, Christian Svend Roy ISIS:151213 dis PREDICTIVE MODELLING ELECTRICITY PRICES FOR SHORT-TERM AND LONGTERM HORIZONS, THE CASE OF THE CZECH REPUBLIC eng Predictive Modelling Electricity Prices for Short-Term and Long-Term Horizons, the case of the Czech Republic / Christian Svend Roy Billinton 2024 ?? stran : digital, PDF soubor Vedoucí práce: Karel Helman Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 Accurate forecasts are a crucial aspect of many industries such as electricity and energy markets for which this paper studies. While many markets have gone through turbulent time, this study aims to apply Autoregressive Integrate Moving Average (ARIMA), Support Vector Regression (SVR) and Long-Short Term Memory (LSTM) models to predicting the daily wholesale electricity prices from the Czech day ahead market. This thesis employs the use of a rolling interval approach to predicting across the year 2022 for short-term horizons and long-term horizon, in which the short-term horizon is defined as the next day’s price, while the long-term horizon being the forecast for the multi-step of the next month. A variance reduction method of log transforming the data is used, while models are also used with the un-transformed data. As a result, Support Vector Regression Model outperformed the other models across both time horizons, while the ARIMA and LSTM model provided closely comparable results on average across the year. Způsob přístupu: Internet data analysis and modeling [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Time series Electricity Price Forecasting ARIMA Support Vector Regression Long-Short Term Memory Rolling Interval Helman, Karel xx0260953 ths Čabla, Adam, 1985- vse2015897119 opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/86399/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/86399 Hlavní práce https://insis.vse.cz/zp/86399/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/86399/posudek/oponent/81931 Oponentura https://insis.vse.cz/zp/86399/priloha/29359 Přiloha k práci https://insis.vse.cz/zp/86399/podrobnosti dc:identifier NEPOSILAT VSKP vse86399 240822 86399