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Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study

Author

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  • Miguel A. Jaramillo-Morán

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain)

  • Daniel Fernández-Martínez

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain)

  • Agustín García-García

    (Department of Economics, Faculty of Economics and Business Sciences, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain)

  • Diego Carmona-Fernández

    (Department of Electrical Engineering, Electronics and Automation, School of Industrial Engineering, University of Extremadura, Avda. Elvas s/n, 06006 Badajoz, Spain)

Abstract

European Union Allowances (EUAs) are rights to emit CO 2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.

Suggested Citation

  • Miguel A. Jaramillo-Morán & Daniel Fernández-Martínez & Agustín García-García & Diego Carmona-Fernández, 2021. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study," Energies, MDPI, vol. 14(23), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7845-:d:685593
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    References listed on IDEAS

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