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On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks

Marcjasz, Grzegorz; Uniejewski, Bartosz; Weron, Rafał - In: International journal of forecasting 35 (2019) 4, pp. 1520-1532

Persistent link: https://www.econbiz.de/10012305383

On the importance of the long-term seasonal component in day-ahead electricity price forecasting

Nowotarski, Jakub; Weron, Rafał - In: Energy economics 57 (2016), pp. 228-235

Persistent link: https://www.econbiz.de/10011698464

Machine learning in energy forecasts with an application to high frequency electricity consumption data

Heilmann, Erik; Henze, Janosch; Wetzel, Heike - 2021

Forecasting plays an essential role in energy economics. With new challenges and use cases in the energy system …

Persistent link: https://www.econbiz.de/10012649104

Forecasting day-ahead electricity prices : utilizing hourly prices

Raviv, Eran; Bouwman, Kees E.; Dijk, Dick van - 2013

Persistent link: https://www.econbiz.de/10009765836

Forecasting daily electricity prices with monthly macroeconomic variables

Foroni, Claudia; Ravazzolo, Francesco; Rossini, Luca - 2019

forecasting daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed …-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). We study the forecasting accuracy …

Persistent link: https://www.econbiz.de/10011987142

Probabilistic electricity price forecasting with NARX networks : combine point or probabilistic forecasts?

Marcjasz, Grzegorz; Uniejewski, Bartosz; Weron, Rafał - In: International journal of forecasting 36 (2020) 2, pp. 466-479

Persistent link: https://www.econbiz.de/10012415156

Deregulated Day-Ahead Electricity Markets in Southeast Europe : Price Forecasting and Comparative Structural Analysis

Hryshchuk, Antanina - 2018

decentralized Western European market. To that end, several price forecasting methods including autoregressive approaches, multiple … influential fundamental factors are determined and performance of forecasting techniques is analysed with respect to the age of …

Persistent link: https://www.econbiz.de/10012927766

Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning

Luo, Jiawen; Ji, Qiang; Klein, Tony; Walther, Thomas - 2021

baseline model forecasts. We show that machine learning generated forecasts provide better forecasting quality and that … forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to …

Persistent link: https://www.econbiz.de/10013239839

Support Vector Regression Based GARCH Model with Application to ForecastingVolatility of Financial Returns

Chen, Shiyi; Jeong, Kiho; Härdle, Wolfgang - Sonderforschungsbereich Ökonomisches Risiko <Berlin> - 2008

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms …

Persistent link: https://www.econbiz.de/10005860742

Mixtures of t-distributions for finance and forecasting

Giacomini, Raffaella; Gottschling, Andreas; Häfke, … - 2007

We explore convenient analytic properties of distributions constructed as mixtures of scaled and shifted t-distributions. A feature that makes this family particularly desirable for econometric applications is that it possesses closed-form expressions for its anti-derivatives (e.g., the...

Persistent link: https://www.econbiz.de/10009735358

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