The field of system identification and signal processing is witnessing significant developments, driven by the increasing availability of data and the need for more accurate and efficient methods. Researchers are exploring innovative approaches to address challenges such as outlier detection, frequency response identification, and spectrum prediction. A key trend is the integration of advanced mathematical techniques, such as Riemannian geometry and fractional Fourier transforms, to improve the robustness and accuracy of these methods. Another area of focus is the development of data-driven approaches for uncertainty model identification and unfalsification, which can provide valuable insights into system behavior and performance. Notable papers in this area include: Leveraging the Christoffel Function for Outlier Detection in Data Streams, which introduces novel methods for outlier detection in data streams. Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering, which proposes a framework for accurate spectrum prediction using adaptive fractional Fourier transforms. Riemannian Change Point Detection on Manifolds with Robust Centroid Estimation, which presents a method for change-point detection in streaming time series data using robust centroid estimation on Riemannian manifolds.