Understanding patterns in financial time series is crucial for improving prediction accuracy in algorithmic trading and risk management. This paper presents a novel AI-based computer vision approach for classifying financial time series. Historical price sequences are transformed into Gramian Angular Difference Field (GADF) images and fed into a convolutional neural network (CNN) for pattern recognition. To interpret the CNN’s decision-making process, we apply Spectral Relevance Analysis (SpRAy), enabling the identification of distinct clusters based on relevance maps. Clustering the images according to their relevance profiles reveals groups with significantly higher predictive performance compared to the full dataset. The corresponding relevance patterns highlight favorable price movement structures and are identified via the associated clusters.

We introduce a novel framework that generalizes $f$-divergences by incorporating locally non-convex divergence-generating functions. Using this extension, we define a new class of pseudo $f$-divergences, encompassing a wider range of distances between distributions that traditional $f$-divergences cannot capture. Among these, we focus on a particular pseudo divergence obtained by considering the induced metric of Bayes Hilbert spaces. Bayes Hilbert spaces are frequently used due to their inherent connection to Bayes's theorem. They allow sampling from potentially intractable posterior densities, which has remained challenging until now. In the more general context, we prove that pseudo $f$-divergences are well-defined and introduce a variational estimation framework that can be used in a statistical learning context. By applying this variational estimation framework to $f$-GANs, we achieve improved FID scores over existing $f$-GAN architectures and competitive results with the Wasserstein GAN, highlighting its potential for both theoretical research and practical applications in learning theory.

In this paper, we develop new techniques for monitoring image processes under a fairly general setting with spatially correlated pixels in the image. Monitoring and handling the pixels directly is infeasible due to an extremely high image resolution. To overcome this problem, we suggest control charts that are based on regions of interest. The regions of interest cover the original image which leads to a dimension reduction. Nevertheless, the data are still high-dimensional. We consider residual charts based on the generalized likelihood ratio approach. Existing control statistics typically depend on the inverse of the covariance matrix of the process, involving high computing times and frequently generating instable results in a high-dimensional setting. As a solution of this issue, we suggest two further control charts that can be regarded as modifications of the generalized likelihood ratio statistic. Within an extensive simulation study, we compare the newly proposed control charts using the median run length as a performance criterion.