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Publicação
Forecasting natural gas day-ahead Title Transfer Facility (TTF) prices using Hidden Markov Models
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Orientador(es)
Resumo(s)
Accurate forecasting of natural gas prices play an essential role in supporting energy policy and guiding operational decisions in the context of Europe’s energy needs. As the Title Transfer Facility hub in the Netherlands serves as the most liquid and representative benchmark in the region, understanding and anticipating its price movements has become increasingly important, particularly in times of market stress.
In this thesis, I develop a Hidden Markov Model to forecast day-ahead natural gas prices at the TTF hub. I use a daily dataset spanning January 2015 to February 2025, incorporating daily returns, volatility, and storage levels to capture both behavioral and fundamental drivers of price dynamics. The model achieved a Mean Absolute Percentage Error of 3.35%, with strong performance across both stable and volatile periods. As part of the evaluation, I also tested the model across multiple short-term horizons (1, 3, and 5-day forecasts) and compared it against a Naive and ARIMA baseline. Hidden Markov Model effectively identified four distinct hidden states, corresponding to different market regimes such as seasonal demand cycles, stable conditions, and periods of extreme stress. By combining regime detection with probabilistic forecasting, the model not
only delivers accurate predictions but also enhances its interpretability, offering valuable insights for stakeholders in trading, policy, and infrastructure planning.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Machine Learning Natural Gas TTF Hidden Markov Models SDG 7 - Affordable and clean energy SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure SDG 11 - Sustainable cities and communities SDG 12 - Responsible production and consumption SDG 13 - Climate action