100积分寻专业人士翻译英文文章

文章标题：A Dynamic Combination Forecast Model for Analysis Transport Volume Time Series

摘要：
Abstract
A dynamic combined forecasting model for transport
freight volume time series prediction is established. The
time-varying combined weights are computed with the
Bayesian posterior probability based on each local
predictor’s performance. This method’s forecast
performance is reliable, because it tracks the real-time
prediction precision of the combined models and adjusts
their credit values (weights) according to their past
predictive error. The forgetting factor is proposed as a
threshold in order to avoid the singular forecasting
model’s performance change so intensely over different
time intervals as to cause unimaginable effect to the latter
online weights computation. In error evaluation system,
the performance of the proposed dynamic combination
forecast model outperforms the singular predictor u

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