Time series forecasting with a prior wavelet-based denoising step

Abstract

We provide an extensive study assessing whether a prior wavelet-based denoising step enhances the forecast accuracy of standard forecasting models. Many combinations of attribute values of the thresholding (denoising) algorithm are explored together with several traditional forecasting models used in economic time series forecasting. The results are evaluated using M3 competition yearly time series. We conclude that the performance of a forecasting model combined with the prior denoising step is generally not recommended, which implies that a straightforward generalisation of some of the results available in the literature (which found the denoising step to be beneficial) is not possible. Even if cross-validation is used to select the value of the threshold, a superior performance of the forecasting model with the prior denoising step does not generally follow.