Forecasting an Accumulated Series Based on Partial Accumulation
提出一种贝叶斯方法，在观测数据很少时预测正连续变量的累积值，适用于季节性稳定的时尚商品销售和优惠券兑换场景，给出点预测和后验分布的精确解析解。
AbstractWe present a Bayesian solution to forecasting a time series when few observations are available. The quantity to predict is the accumulated value of a positive, continuous variable when partially accumulated data are observed. These conditions appear naturally in predicting sales of style goods and coupon redemption. A simple model describes the relation between partial and total values, assuming stable seasonality. Exact analytic results are obtained for point forecasts and the posterior predictive distribution. Noninformative priors allow automatic implementation. The procedure works well when standard methods cannot be applied due to the reduced number of observations. Examples are provided.KEY WORDS: Bayesian inferencePredictionStable seasonalityTime series