Multiscale analysis approach for novelty detection in adaptation plot

Recently, a multipurpose adaptive technique of evaluation of complicated behaviour of complex dynamic systems was introduced. That cognitive signal processing technique is based on evaluation and visualisation of unusual weight increments of sample-by-sample gradient descent adapted models. It has appeared that important attributes of complicated behaviour of complex dynamical systems can be captured and evaluated via much simpler (lower-dimensional) adaptive predictors. The visualisation method is called an Adaptation Plot (AP). So far, the weakness of AP is that AP needs manual tunning to reveal the important and otherwise hidden system (signal) attributes that emerge for a specific single setup of parameter. This paper introduces a multiscale approach for detection sensitivity of AP to overcome its single-setup limited interpretability.