This work presents an open-source tool to predict natural system responses by transforming the frequency spectrum of predictor variables to create a response that better resembles observations. The R package, namely WAvelet System Prediction (WASP), is based on a discrete wavelet transform (DWT)-based variance transformation method. We further introduce the maximal overlap DWT (MODWT)-based variance transformation which allows the method to be used in forecasting applications. We also develop the method to include an unbiased estimator that mitigates the well-known issue of edge effects in wavelet transforms. The predictive model in the method is a k-nearest neighbor (knn) approach. The main functionalities of the software include: (1) transforming the system predictors, (2) identifying significant predictors corresponding to the response, (3) predicting target response using the knn. Results of predicting sustained drought anomalies across Australia show clear improvements in predictive skill compared to the use of untransformed predictors.

这项工作提出了一种开放源代码的工具，可以通过转换预测变量的频谱来创建自然响应，从而更好地类似于观测结果，从而预测自然系统的响应。R程序包，即WAvelet系统预测（WASP），基于基于离散小波变换（DWT）的方差转换方法。我们进一步介绍了基于最大重叠DWT（MODWT）的方差变换，该方法允许将这种方法用于预测应用程序。我们还开发了包括无偏估计器的方法，该估计器减轻了小波变换中边缘效应的众所周知的问题。该方法中的预测模型是k最近邻（knn）方法。该软件的主要功能包括：（1）转换系统预测变量，（2）识别与响应相对应的重要预测变量，（3）使用knn预测目标响应。与未转换的预测器相比，整个澳大利亚持续干旱异常的预测结果表明，预测技能有了明显的提高。