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基于部分累计数据预测累计总量AI 解读

We 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…

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时间序列解聚与预测：贝叶斯分析AI 解读

The problem of temporal disaggregation of time series is analyzed by means of Bayesian methods. The disaggregated values are obtained through a posterior distribution derived by using a diffuse prior on the parameters. Further analysis is carried out assuming alternative conjugate priors. The means of the different posterior distributions are shown to be equivalent to some sampling theory results. Bayesian prediction intervals are obtained. Forecasts for future disaggregated…