Performance Measures for Evaluating the Accuracy of Time Series Hybrid Model Using High Frequency Data

Abstract

Given that the traditional ARIMAX model has rarely been applied to any of the climate change and environmental agents, which are the most cognate agents with associated exogenous variables; to neutralize the model for a better and enhanced prediction of the system, a distributional form of the error term that is robust and sufficient in capturing and accommodating both the external covariate(s) and high frequency data is required. This study therefore evaluates the forecasting accuracy of two forecasting models namely ARIMAX and log-ARIMAX. The monthly adjusted high frequency data recorded by four Oil and Gas companies from 2005 – 2020 were used. The forecastability of the two models was evaluated with different error matrices. The effect of Akaike Information Criterion (AIC) and the linear correlation on candidate models among the considered oil spill data tested were discussed. Results for ARIMAX and LOG-ARIMAX Models selection with respect to AIC show that log-ARIMAX is more efficient and performed better than the traditional ARIMAX model for observations characterized by kurtosis, skewness, outliers, high frequency and large fluctuation series with heavy tailed traits as seen in environmental data.