局域法邻近点选取对供水量预测精度的影响
Influence of select the local-region method nearest neighbours on water supply forecasting accuracy

混沌局域法预测模型适用于非线性、非平稳的城市日供水量预测，而邻近相点个数的选取对该模型预测精度有直接影响。传统方法通常以嵌入维m作为参考值，凭经验选取m+1个邻近相点，且仅使用欧式距离法计算当前相点距离，法反映相点的运动趋势，易引入伪邻近相点，导致预测精度的降低。鉴于此，将演化追踪法引入城市日供水量预测，通过挖掘邻近相点的历史演化规律对参考样本进行优选，以提高预测精度。最后，采用实际日供水量数据验证所提出方法，结果表明，运用演化追踪法优选邻近相点能显著提高日供水量预测精度，预测平均绝对误差由2.501%降低到1.683%。
The chaotic local-region forecasting model is suitable for nonlinear and non-stationary urban daily water supply forecast, and the neighbourhood selection has a direct impact on the model prediction accuracy. The traditional method usually takes the embedded dimension m as a reference, and selects m+1 nearest neighbours by experience. It usually introduces the pseudo nearest neighbours, which leads to the reduction of the prediction accuracy. Accordingly, the evolutionary tracing method is introduced into the prediction of urban daily water supply. By mining the historical evolution of nearest neighbours, the reference samples are optimized to improve the prediction accuracy. The proposed method is validated by the actual daily water supply data. The results show that the optimal approach is significantly improved by using evolutionary tracing method, and the average absolute error is reduced from 2.501% to 1.683%.