For time series forecasting, the mobile holiday effect typically brings certain difficulty in obtaining accurate forecasts for monthly and quarterly series since it can result in enormous disturbances for modeling, especially for the sequences with limited information and much uncertainty. This study proposes a discrete grey seasonal model by cycle accumulation generation as an alternative approach to seasonal time series forecasting to effectively address such issues. Moreover, the logarithmic transformation technique is introduced to enhance ...
