A NEW APPROACH FOR MODELLING SKEWED-SEASONAL TIME SERIES DATASETS

Abstract

New models applicable to skewed distributions have been developed 
since the transformation changes the structure of the original series. These models fail when applied to datasets that exhibit seasonality and skewness, as accurately modeling the data structure aids in forecasting and planning. The study proposed the Skewed Seasonal Model (SSM) for the simulated data. The results showed that the proposed Skewed-Seasonal Model (SSM) accounted for the variability in the series better than the AR model. The Skewed-Seasonal Model (SSM) approach exhibited better goodness of fit, in addition to higher forecasting ability than the AR model. The forecast evaluation metrics indicated that the forecast evaluation of the Skewed-Seasonal Model (SSM) had lower values, making the proposed Skewed-Seasonal Model (SSM) more effective than the standard Autoregressive model in capturing and predicting the behavior of skewed-seasonal time series.