基于多尺度相关向量机的城市日用水量预测

为提高日用水量预测精度,提出一种基于多尺度相关向量机的预测模型。通过静态小波分解将用水量非平稳时间序列分解为不同尺度的平稳时间序列,然后在分解后的各子序列分别建立相关向量机回归模型进行预测,最后通过小波逆变换将各子序列预测结果整合得出原始用水量时间序列的预测值。在实例分析中分别利用多尺度关联向量机模型和单尺度相关向量机预测模型对实际用水量进行预测分析。结果表明,前者具有更高的预测精度,可应用于城市日用水量的预测。

Forecast of urban daily water consumption based on multi-scale relevance vector machine

In order to improve the forecast precision of daily water consumption,the paper proposed a forecast model based on multi-scale relevance vector machine (MSRVM). The non-stationary time series of daily water consumption are decomposed into different scales stationary time series by stationary wavelet transform.Then it established the regression model of relevance vector machine in each scale to predict respectively,and finally employed the forecast results of the RVM outputs at all the scales to reconstruct the forecast value of the original daily water consumption time series through the inverse wavelet transform. Application examples show that compared with mono-scale RVM model, the MSRVM model has the better precision and can be applied in the forecast of daily water consumption.