Browsing by Author "Arthur, Doris"

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    Dilemmas in model selection in time series analysis
    (KNUST, 2018-06) Arthur, Doris
    This study seeks to resolve two important dilemmas in model selection in time series analysis. These are to compare the performance of the graphical and the information criterion methods in selecting the true model. In addition, Yu et al. (2005) relative precision performance stability was modi_ed. For the graphical and information criterion comparison, dataset from ARIMA models were simulated. Also the cocoa production and rainfall datasets in Ghana were used to validate the modified relative precision performance stability of Yu et al. (2005). It was observed from the study that, in comparison to the performance of the graphical method and the Akaike information criterion (AIC) in selecting the ARIMA models, the information criterion performs better than the graphical method. Also, in verifying for the size of the evaluation sets in forecasting, whether to select a single model or combine the models of di_erent models, our findings showed that the size of the evaluation sets may not influence the decision of selecting or combining since 97% of the decisions were to combine the models. In addition to that, though there was a modification on the computations of the relative prediction performance stability formerly utilized by [Yu et al., 2005], the decision rule still remains the same. Hence, whether the use of the mean or median on different the size of evaluation sets and interval, the combining strategy still outperforms.