Challenges in anomaly and change point detection
Mădălina Olteanu, Fabrice Rossi, Florian Yger · 2022
Auto-Adaptive Laplacian Pyramids (ALP) is an iterative kernel-based regression model.It constructs a multi-scale representation of the train data, where the multi-scale modes are average residuals.In this work, we propose two extensions of the model.The first is a hybrid approach that combines ALP with Empirical Mode Decomposition to provide localization in the frequency domain.The second modifies ALP to fit datasets with non-uniform noise, which is achieved by computing the optimal stopping criterion in a point-dependent manner.Experimental results demonstrate these models for solar energy prediction and for forecasting epidemiology infections.