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

Abstract

European Union Allowances (EUAs) are rights to emit CO2 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%.

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

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Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

“…Empirical mode decomposition and variational mode decomposition are two such approaches, which separate time series into numerous subseries, each with correct oscillation behaviors. Both techniques have been used in a variety of forecasting tools, including autoregressive integrated moving averages, convolutional neural networks, multilayer perceptron, support vector regression, and long short-term memory models [42][43][44][45]. For instance, the authors of ref.…”

Prediction of State Civil Apparatus Performance Allowances Using the Neural Network Backpropagation Method

“…Previous research on predictions of European Union Allowances (EUAs) benefits has been carried out by Jaramillo-Morán et al [11], which is a right to emit CO2 that can be sold or purchased by companies to reduce greenhouse gas emissions. This study uses the Neural Network method with the Multilayer Perceptron (MLP) and Long Short-Term Memories (LSTM) algorithms, which have the best results Mean Absolute Percentage Error (MAPE) of 2.91% for the prediction of the first datum and 5.65% for the second datum twenty, with an average value of 4.44%.…”