Optimized dynamic exponential smoothing model and its applications

From analyzing the deficiency of the traditional model, we put forward the concept of dynamic smoothing parameter and a new class of quadratic exponential smoothing model, the main features of our model are that it's unnecessary to put any initial value in smoothing time series and the weight number in the formula is always adjusting with the parameter self_actingly. Aiming the square sum of error (SSE), we also construct the steepest_descent algorithm to iterate and select an optimal parameter for optimizing the new model. By deducting mathematically,some questions, i.e., the smoothing parameter was static and determined only by one's experiences, and the smoothing initial value is difficulty to determine and leads to a deviation easily, are completely resolved. At last, the total sales of consumer goods in China is forecasted by using the new model, and the result apparently is superior to the traditional model's one.