Seasonal Differences as a Tool for Choice of Model in Descriptive Time Series Analysis
Owolabi, T. W.
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This thesis discusses the choice of model for time series decomposition using seasonal differences. The ultimate objective is to provide the basis for choosing between additive, multiplicative, and mixed model for decomposition for any time series data. The method adopted is the use of the row and column variances of the seasonal differences arranged in a Buys- Ballot table. The results show that the column and row variances of the seasonal differences are constant for Addictive model and the mixed model and depend on the seasonal effect for the multiplicative model. In view of this, Five parametric and four non parametric tests for constant variances were used for the test and it has been proposed that row variances of the seasonal differences in Buys-Ballot table be used to choose between additive, multiplicative and mixed models for decomposition of a study time series data. It’s further suggested that to choose between additive and mixed model, the seasonal index of the series has to be isolated.
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