Statistical Analysis of the Costs of OPEC Crude Oil

V. Swapna¹ , Kanthala Sampath Kumar² , V.V. Hara Gopal³

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.4 , pp.1-9, Aug-2020

Online published on Aug 31, 2020

Copyright © V. Swapna, Kanthala Sampath Kumar, V.V. Hara Gopal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
In this study, Seasonal Na?ve, ETS (Error, Trend, Seasonal), Neural Networks (NN), and ARIMA methods are applied for forecasting the Time series data and out of these NN is found to be the most efficient method. The present study employs measures, ME, RMSE, MAE, MASE and ACF1 to evaluate the performance of the model. These are used to compare the performance of the proposed technique and that of ARIMA and other methods. The study adopts the data of the costs of crude oil from the Organization of the Petroleum Exporting Countries (OPEC). Linear and non-Linear methods of forecasting the prices of crude oil among the OPEC are discussed, and their advantages and disadvantages are also illustrated for improving the performance and prediction. The revealing results of the proposed i.e. NN method outperforms the others in forecasting accurate results

Key-Words / Index Term :
Time Series, Crude Oil Price, Neural Networks (NN), Auto Regressive (AR), Moving Average (MA), Stationary, Linear Model, non-Linear Model

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