Abstract:

Many studies have been done to prove that combining forecast methods gives a better predictive performance relative to individual forecasts. This paper compared the single forecast method and the combined methods in predicting time series data. The study used annual oil revenue for the period 1981–2019 from the Central Bank of Nigeria (CBN), which were divided into two sets: the Training Set (TS) which covered the period 1981–2010 and the Test Set (VS) which covered 2011–2019. The study adopted autoregressive integrated moving average (ARIMA), simple exponential smoothing (SES), and Holt’s linear trend (Holt) as the individual forecast methods; it also adopted outperformance of forecasts (OPF) and weighted mean (WM) as weight selection methods. The forecast methods were applied to the Training Set after which they were combined. Two combined methods CM1 (ARIMA + SES) and CM2 (ARIMA + SES + Holt) were obtained. The result of this study showed that simple exponential smoothing (SES) as an individual forecast method is better and less risky than the combined methods for forecasting time series.