Short-term sales forecasting with change-point evaluation and pattern matching algorithms

摘要

The hotel and car manufacturing industries share many common points in their sales forecasting. For example, both are greatly affected by the fluctuation of economy, and closely related to the inertia. According to the principle characters of forecasting problem concerning these two kinds of industries, a short-term quantitative sales forecasting model is proposed based on the economic fluctuation analysis and the naı¨ve forecasting technology. The sales time series and its curve are used to construct this model. The relative concepts of the model are presented and corresponding algorithms are brought forward. Firstly, economic fluctuation of products sales is analyzed and the historical patterns of economic fluctuation change are divided. According to the geometric characteristics of a sales curve, the best historical matching for the current status is then found out, which corresponds to the process of activating the historical experiences of a manager. Finally the changing trend of the sales curve in the next period is determined, from which the short-term sales forecasting results can be obtained. The number of scattered guests of a hotel and the short-term sales for cars manufactured by a factory are forecasted by means of the model, which shows satisfactory forecasting accuracy. In fact, the forecasting approach proposed herein is the mathematical representation of the naïve forecasting method that is a kind of regular deduction based on the similarity between historical pattern and current status. Thus, this approach is good at forecasting the time series with the similarity between historical pattern and current status no matter whether the time series is seasonal or not, and gives better forecasting accuracy than ARMA and ANN models.