基于周期外延法的监测效应量灰色时序组合预测模型

针对单一模型在大坝效应量监测数据序列拟合和预测方面存在的不足，采用Verhulst模型拟合监测数据序列中的趋势性成分，采用周期外延模型拟合监测数据序列中的周期性成分，采用自回归AR(p)模型拟合监测数据序列中的随机性成分，得到一种新的组合模型，并给出了一个工程实例。该组合模型丰富了监测效应量预测方法，可提高监测效应量的整体预测精度，深化对监测效应量变化规律的认识。

Combinatorial Forecast Model of Monitoring Effect Quantities Based on Periodic Extensional Method and Grey-Time Serial Model

In view of the shortcomings of single model used to simulate and forecast the data sequence of dam monitoring effect quantities, a new combinatorial model is constructed and an engineering example is given in this paper. In this combinatorial model, trend component, periodic component and random component of the monitoring data sequence are respectively simulated by Verhulst model,periodic extensional model and AR(p) model. The forecast methods for monitoring effect quantities can be enriched and the overall forecast accuracy can be improved with this combinatorial model, and our understanding of the variation regularity of monitoring effect quantities can also be deepened.