Seasonal learning based ARIMA algorithm for prediction of Brent oil Price trends - Theerthagiri, Prasannavenkatesan; Ruby, A. Usha (Multimedia tools and applications, 2023)
The global economy relies heavily on the worldwide crude oil market. This work presents a crude oil price prediction technique using a time-varying trend. It works by decomposing crude oil price trends over time to characterize changes using a variable time window and determine the price trend in terms of time series. Seasonal Auto Regressive Integrated Moving Average (SARIMA) methodology has been developed in order to predict crude oil price fluctuations over time. The proposed SARIMA model predicts the prices using the weighted average method and accuracy estimation methodology with the feedback error analysis method. Various SARIMA models are evaluated, and best fit relative quality orders have been selected based on the Akaike information criterion. The prediction results of the proposed SARIMA approach are analyzed with performance error metrics and Kurtosis values. Further, the results of the SARIMA model were compared with the existing algorithms. The proposed SARIMA produces higher prediction accuracy with a very much reduced mean absolute error of 30 to 59% compared to the existing approaches.