Leveraging Non-Decimated Wavelet Packet Features and Transformer Models for Time Series Forecasting

arXiv:2403.08630v1 Announce Type: cross
Abstract: This article combines wavelet analysis techniques with machine learning methods for univariate time series forecasting, focusing on three main contributions. Firstly, we consider the use of Daubechies wavelets with different numbers of vanishing moments as input features to both non-temporal and temporal forecasting methods, by selecting these numbers during the cross-validation phase. Secondly, we compare the use of both the non-decimated wavelet transform and the non-decimated wavelet packet transform for computing these features, the latter …

abstract analysis article arxiv cs.lg features forecasting machine machine learning moments numbers series stat.me temporal time series time series forecasting transformer transformer models type wavelet