Forecasting of Monthly Crude Oil Prices in Kenya Using Comparative Time Series Models

Crude oil is one of the most vital products that ever existed and variation in prices affects all sectors of the economy and variation in its prices is very crucial. Therefore, without an accurate and appropriate predictive model for crude oil prices, it has proven difficult to predict future oil prices. Therefore, appropriate modeling is crucial for the oil companies to adjust strategies used in the production and supply as well as structural optimization. This study sought to fit several time series forecasting models namely; ARIMA, Naïve, Seasonal Naïve, Time Series Linear Model (TSLM), and Exponential Smoothing (ETS) to model and forecast crude oil prices. The study used Kenya’s monthly crude oil prices from Jan 2003 to Dec 2023, giving a sample of 252 observations. The selection of the best model was based on the minimization of the information criteria, where ARIMA, ETS, and Seasonal Naïve attained an AIC of 1294.193, 1846.780, and 1403.821, respectively. Similarly, the models attained the BIC of 1304.991, 1863.071, and 1450.489, respectively. Since the Naïve and TSLM could not provide the AIC and BIC, model selection was entirely based on the forecast accuracy measures. From the results, the Naive model reported the lowest RMSE (19.6329), indicating that it has the smallest average squared error, with the lowest MAE (15.5212), suggesting it has the smallest average absolute error. Besides, the Naive model reported an ME (1.9819) which is relatively low but not the lowest. The automatic ETS model has a slightly lower ME (3.478211). The naive model reported lower MPE and MAPE values (-3.9525 and 26.8022, respectively) compared to most models, indicating less percentage error. Similarly, a lower MASE and RMSSE were reported by the naïve model, (0.7343624) and RMSSE (0.7228792), respectively, indicating that the model performs well relative to forecasting crude oil prices in Kenya. The naïve model demonstrated a higher consistency and reliability in forecasting crude oil prices in Kenya.