Forecasting Method Selection Using ANOVA and Duncan Multiple Range Tests on Time Series Dataset

Selection of a suitable forecasting technique is of
prime importance in order to obtain a better prediction result. This paper demonstrated the use of two statistical approaches namely, Analysis of Variance (ANOVA) and Duncan multiple range tests for determining the performance of different forecasting methods. Three forecasting methods were chosen and compared: regression, decomposition, and ARIMA. Data from monthly incidence of Salmonellosis in US from 1993 to 2006 was collected and used for technical analysis. ANOVA was initially used to identify significant difference between the actual data and three forecasting methods. Based on the results from ANOVA, selection of appropriate method was conducted using Duncan multiple range tests. The results showed that both regression and ARIMA could be used in the Salmonellosis data. On the contrary, decomposition method yielded the least performance and is not suitable for being applied on the
available dataset