Forecasting daily month-ahead TTF gas prices using a combination of preprocessing and machine learning techniques

Abstract

Although energy commodity price forecasting has been around for quite some time, up until recently, especially in Europe, it mostly concerned other energy commodities than gas. That is why in this work, a forecasting model is presented for the single day forecast of the daily VWAP price of the TTF month-ahead gas contract from 2020/01/02 until 2023/08/03. A model combining machine learning and data preprocessing is proposed. First a decomposition of the gas price is produced by a combination of Variational Mode Decomposition (VMD) and Independent Component Analysis (ICA). With these decompositions an initial volatility regime split is chosen because of the unusual characteristics of the dataset. During the forecasting period the Russo-Ukrainian war started, and the economy was recovering from the COVID-19 crisis, causing the gas price to surge to levels previously unknown. Using the decompositions as input, a final price prediction has been made using a Gated Recurrent Unit Neural Network (GRUNN) and Support Vector Regression (SVR), amongst others. The proposed model is compared to a selection of benchmarks, one of which is the naive forecast. To conclude, a selection of exogenous variables is added to the model to improve the performance. Gas storage and the UK NBP gas price are chosen for their specific characteristics. The best performance the model exhibits is between 0.38% MAPE for the low volatility regime, and 1.4% MAPE for the high volatility regime.