Oil Price Factors: Forecasting on the Base of Modified ARIMA Model

Anthony Msafiri Nyangarika; Bao Jun Tang

doi:10.1088/1755-1315/192/1/012058

Oil Price Factors: Forecasting on the Base of Modified ARIMA Model

Anthony Msafiri Nyangarika, Bao Jun Tang

科研成果: 期刊稿件 › 会议文章 › 同行评审

7 引用（Scopus）

摘要

The paper proposes the modification of ARIMA model for finding the parameters of estimation and forecasts using exponential smoothing. The study use data Brent crude oil price and gas prices in the period from January 1991 to December 2016. The result of the study showed an improvement in the accuracy of the predicted values, while the emissions occurred near the end of the time series. It has minimal or no effect on other emissions of this data series. The study suggests that investors can predict prices by analyzing the possible risks in oil futures markets.

源语言	英语
文章编号	012058
期刊	IOP Conference Series: Earth and Environmental Science
卷	192
期	1
DOI	https://doi.org/10.1088/1755-1315/192/1/012058
出版状态	已出版 - 5 11月 2018
活动	2018 2nd International Conference on Power and Energy Engineering, ICPEE 2018 - Xiamen, 中国期限: 3 9月 2018 → 5 9月 2018

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Nyangarika, A. M., & Tang, B. J. (2018). Oil Price Factors: Forecasting on the Base of Modified ARIMA Model. IOP Conference Series: Earth and Environmental Science, 192(1), 文章 012058. https://doi.org/10.1088/1755-1315/192/1/012058

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abstract = "The paper proposes the modification of ARIMA model for finding the parameters of estimation and forecasts using exponential smoothing. The study use data Brent crude oil price and gas prices in the period from January 1991 to December 2016. The result of the study showed an improvement in the accuracy of the predicted values, while the emissions occurred near the end of the time series. It has minimal or no effect on other emissions of this data series. The study suggests that investors can predict prices by analyzing the possible risks in oil futures markets.",

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AB - The paper proposes the modification of ARIMA model for finding the parameters of estimation and forecasts using exponential smoothing. The study use data Brent crude oil price and gas prices in the period from January 1991 to December 2016. The result of the study showed an improvement in the accuracy of the predicted values, while the emissions occurred near the end of the time series. It has minimal or no effect on other emissions of this data series. The study suggests that investors can predict prices by analyzing the possible risks in oil futures markets.

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Oil Price Factors: Forecasting on the Base of Modified ARIMA Model

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