The Bayesian Approach to Forecasting Oracle（贝叶斯方法预测甲骨文）.pdf

The Bayesian Approach to Forecasting An Oracle White Paper Updated September 2006 The Bayesian Approach to Forecasting INTRODUCTION The Bayesian approach uses a combination of a priori andpost priori knowledge to The main principle of forecasting is to find model time series data. That is, we know if we toss a coin we expect a probability the model that will produce the best forecasts, not the best fit to the historical of 0.5 for heads or for tails—this is a priori knowledge. Therefore, if we take a coin data. The model that explains the historical and toss it 10 times, we will expect five heads and five tails. But if the actual result data best may not be best predictive is ten heads, we may lose confidence in our a priori knowledge. This may be model. explained by a change to the coin that was introduced to alter the probability—this is post priori knowledge. Another example of post priori knowledge is future price change or marketing promotion that is likely to alter the forecast. The main principle of forecasting is to find the model that will produce the best forecasts, not the best fit to the historical data. The model that explains the historical data best may not be best predictive model for several reasons. • The future may not be described by the same pr