Forecasting energy commodity prices using neural networks

DOI10.1155/2012/289810zbMath1254.91683OpenAlexW1978982237WikidataQ58697772 ScholiaQ58697772MaRDI QIDQ1929898

Rita L. D'Ecclesia, Massimo Panella, Francesco Barcellona

Publication date: 10 January 2013

Published in: Advances in Decision Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2012/289810

zbMATH Keywords

machine learning approachprice modeling

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

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Uses Software

• sapa
• PRMLT

Cites Work

• A hierarchical procedure for the synthesis of ANFIS networks
• Boosting GARCH and neural networks for the prediction of heteroskedastic time series
• Prediction in dynamic models with time-dependent conditional variances
• Generalized autoregressive conditional heteroscedasticity
• Neural networks with quantum architecture and quantum learning
• CALIBRATION OF MULTIFACTOR MODELS IN ELECTRICITY MARKETS
• Spectral Analysis for Physical Applications
• Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• The singular value decomposition and long and short spaces of noisy matrices
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