Abstract
Any nation’s growth depends on the trend of the price of fuel. The fuel price drifts have both direct and indirect impacts on a nation’s economy. Nation’s growth will be hampered due to the higher level of inflation prevailing in the oil industry. This paper proposed a method of analyzing Gasoline and Diesel Price Drifts based on Self-organizing Maps and Bayesian regularized neural networks. The US gasoline and diesel price timeline dataset is used to validate the proposed approach. In the dataset, all grades, regular, medium, and premium with conventional, reformulated, all formulation of gasoline combinations, and diesel pricing per gallon weekly from 1995 to January 2021, are considered. For the data visualization purpose, we have used self-organizing maps and analyzed them with a neural network algorithm. The nonlinear autoregressive neural network is adopted because of the time series dataset. Three training algorithms are adopted to train the neural networks: Levenberg-Marquard, scaled conjugate gradient, and Bayesian regularization. The results are hopeful and reveal the robustness of the proposed model. In the proposed approach, we have found Levenberg-Marquard error falls from − 0.1074 to 0.1424, scaled conjugate gradient error falls from − 0.1476 to 0.1618, and similarly, Bayesian regularization error falls in − 0.09854 to 0.09871, which showed that out of the three approaches considered, the Bayesian regularization gives better results.
Highlights
The transportation industry has come a long way from horses and mules in the early days to railways, airlines, cruises, municipal transportation companies, cargo tracking, and express delivery services in today’s world
Authors in [20] recommended an artificial intelligence (AI) and informationdriven approach to analyze Saudi Arabia's energy markets. Their model GANNATS is a combination of data mining (DM), genetic algorithm (GA), and artificial neural networks (ANN) along with timeseries (TS) analysis, and the design, training, validation, and testing of this model have been done on actual historical market data
The Nonlinear autoregressive neural network (NAR-neural network (NN)) is used for the gasoline and diesel dataset
Summary
The transportation industry has come a long way from horses and mules in the early days to railways, airlines, cruises, municipal transportation companies, cargo tracking, and express delivery services in today’s world. This industry finds its uses in moving people, animals, and goods by land, air, or sea, and as a global necessity, generates revenue worth billions of dollars. The transportation industry services majorly rely on the usage and pricing of gasoline and diesel, which are prone to fluctuations worldwide. The United States, being the most significant consumer, has
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