Analysis of Multi-Input ARIMA Interventions with Additive Outlier for Forecasting Price of Crude Oil West Texas Intermediate

Abstract

Crude oil is a liquid characterized by a thick texture and blackish color. It is composed of complex hydrocarbon compounds with various benefits that are spread around the world. Crude oil derived from fossil fuels can be used as primary fuels, such as gasoline, and is the most important of the energy resources. Based on that, crude oil play a crucial role in the global economy movement because can be used as the main sources of energy all over the world. However, one of the benchmarks for crude oil from the USA is West Texas Intermediate (WTI). Known to produce high-quality oil, the price of crude oil of WTI fluctuates. In addition, fluctuations occur because of several factors, such as the availability of oil supplies, the embargo on oil imports, and the COVID-19 pandemic. The research aims to analyze price forecasting that occurs over the next five months and the accuracy level of the method used. The data that exists outliers is usually removed from forecasting that contains outliers, but that can affect the estimation result in the model. So, in this research intervention and outlier factors are added to the research to overcome the constraints In this study, the Multi-Input ARIMA Intervention and Additive Outlier (AO) method are used by modelling ARIMA pre-intervention and then. After that, the procedure is adding intervention factorsand additive outlier with iterative procedures. Multi-Input ARIMA Intervention and Additive Outlier (AO) are used to determine the magnitude of fluctuations that occur. Data shocks causing outlier data can be used by adding AO factors. WTI oil price data was retrieved from investing.com with monthly data from January 2011 to June 2023. Based on the results of Mmulti-Iinput ARIMA intervention with Additive Outlier method, it has been determined that the movement of WTI oil prices in the next five months will increase compared to the last five periods of actual data. Because of incrased price of crude oil, it will impact of the economic growth all over the world. So, the government better controlled the price of crude oil at lower price. . withMulti-Input ARIMA interventions resulting in AIC, MAPE, and RMSE model each 941.490, 6.979%, and 5.913 . So, Multi-Input and AO proven can be used to forecast data with fluctuate that data occur.

Keywords

• crude oil
• arima
• multi-input
• intervention.