基于滑动窗口的一类非负可变权组合预测方法

针对基于结果的组合预测赋权问题,通过引入预测残差数据的变异系数和滑动窗口模型,给出一类基于滑动窗口和改进变异系数的组合预测时变权重确定方法.将传统基于预测数据层面的变异系数转移到预测残差数据层面,能有效消除传统变异系数由于数据数量级引起的数据变异程度被弱化的情况.结合滑动窗口模型,对已有的赋权方法和提出的基于改进变异系数的赋权方法进行调整,实现非时变权重向时变权重的过渡.实例分析表明,改进变异系数的有效性以及滑动窗口技术的引入能够有效提高组合预测精度.

Nnon-negative variable weight combination forecasting method based on sliding window

For the weighting method in combination forecasting with multiple single predictions, a non-negative time-variant weighting method is given by combining the sliding window model and an introduced modified variation coefficient. Traditional variation coefficient on the level of predicted data is transformed to the level of predicted residual error data, in which the affection of mean in high level is deleted to avoid traditional variation coefficient in low level. Besides, by introducing the sliding window model, current existed weighting methods and the proposed weighting based on modified variation coefficient are improved, and the transformation from time-invariant weights to time-variant weights is realized. The numerical study shows the validity of the developed modified variation coefficient, which also shows the efficiency of the sliding window model in improving the combined forecasting accuracy.