Variable step-size evolving participatory learning with kernel recursive least squares applied to gas prices
Eduardo Ravaglia Campos Queiroz1, Kaike Sa Teles Rocha Alves2, Fernando Luiz Cyrino Oliveira1
1Department of Industrial Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, RJ Brazil.
Evolving Systems
|April 16, 2024
Summary
This study introduces a new machine learning model for accurate time series forecasting of diesel oil prices. The Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares (VS-ePL-KRLS) model shows improved accuracy over existing methods.
Area of Science:
- Data Science
- Machine Learning
- Econometrics
Background:
- Accurate prediction models are crucial for business decision-making.
- Machine learning in time series forecasting is vital for processing information and uncovering knowledge.
Purpose of the Study:
- To develop and evaluate a novel machine learning model for forecasting weekly diesel oil prices.
- To assess the model's accuracy and computational performance for biweekly and monthly horizons.
Main Methods:
- Implementation of the Variable step-size evolving Participatory Learning with Kernel Recursive Least Squares (VS-ePL-KRLS) model.
- Application to forecast S500 and S10 diesel oil prices at the Brazilian level.
Main Results:
- The VS-ePL-KRLS model demonstrated superior accuracy compared to existing models in the literature.
- The model maintained computational performance across all analyzed time series.
Conclusions:
- The VS-ePL-KRLS model offers an effective solution for time series forecasting of commodity prices.
- The model provides enhanced accuracy without compromising computational efficiency.
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