A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies. (January 2021) - Jiang, Ze; Rashid, Md. Mamunur; Johnson, Fiona; Sharma, Ashish
Abstract: 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. Highlights: Open source R-package WASP for modelling and predicting natural system responses. The package modulates the variance in wavelet transformations to improve the match between predictors and the response. The inclusion of an alternative wavelet transform overcomes the issue of future dependence in discrete wavelet transform. The approach is demonstrated with an application to characterise drought using climatic indicators across Australia.