Classification tree methods for panel data using wavelet-transformed time series

TL;DR: In this article , simultaneous multi-site measurements were carried out in NE (Northeast) Spain from 2011 to 2020, using diffusion tubes, and the highest NH3 concentrations were recorded at the traffic site, followed by those measured at the urban background site (2.1 μgm-3).

TL;DR: The experimental results demonstrate that the proposed method not only can handle multivariate time series with different lengths but also is more efficient and effective than the existing methods for the MTS data mining.

TL;DR: This work proposes a novel method to update the interval forecast adaptively using root mean square prediction errors calculated from the latest data batch, and uses wavelet-transformed data to capture long time variable information and conditional inference trees for the underlying regression tree model.

TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.

TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.