article · International Journal of Intelligent Systems
Predicting gasoline prices accurately is critical for energy planning, market risk management, and formulating effective public policies. This research evaluates an adaptive network-based fuzzy inference system to forecast gasoline prices using three decades of historical data from the United States Energy Information Administration, covering 1993 to 2023. By blending fuzzy logic with artificial neural networks, the model effectively maps complex nonlinear trends in energy prices. The input data incorporates temporal components such as year, month, and day, alongside prior weekly prices. Inclusion of previous prices significantly improves prediction accuracy. When evaluated alongside traditional time series methods such as autoregressive integrated moving average and vector autoregression models, the fuzzy inference model achieves high precision, reaching an evaluation score of 0.9970 and a correlation of 0.9985. These capabilities support strategic investment, resource allocation, and policy assessment in volatile energy markets.
Fluctuating fuel costs directly affect transport, supply chains, and consumer budgets. Reliable price forecasting tools help energy firms make sound investment decisions and manage market volatility risks. At the same time, accurate forecasts give policymakers clearer insights into how regulatory actions and sustainability measures interact with real-world fuel prices and consumer behaviour.
The model appears applied and tested on historical data, representing an analytical tool ready for integration into decision-support software. Potential users include commercial energy companies seeking to optimise resource allocation and manage pricing risks, as well as regulatory bodies assessing market policy impacts. Moving to commercial deployment would require embedding the forecasting framework into operational enterprise risk management or market analytics platforms.
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This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.
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DOI: 10.1155/2024/8462056
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