Bayesian Forecasting With Stable Seasonal Patterns
提出一个乘法季节预测模型，利用季节内新事件到达的信息，通过贝叶斯方法递归更新对季节总体的预测，适用于累积事件的短期与长期预测。
A multiplicative seasonal forecasting model for cumulative events in which, conditional on end- of-season totals being given and seasonal shape being known, it is shown that events occurring within the season are multinomially distributed is presented. The model uses the information contained in the arrival of new events to obtain a posterior distribution for end-of-season totals. Bayesian forecasts are obtained recursively in two stages: first, by predicting the expected number and variance of event counts in future intervals within the remaining season, and then by predicting revised means and variances for end-of-season totals based on the most recent forecast error.