Vitalie Spinu
As a senior machine learning scientist at Adyen, my current focus lies in the development of models and explainability tools to monitor and comprehend the intricacies of payment processes. My primary areas of interest encompass Bayesian probabilistic modeling, machine learning explainability, and causality. In my past roles as a freelance professional, employed data scientist, and university researcher, I have engaged with a wide array of applied modeling challenges, such as churn modeling, matching engines, anomaly detection, recommender systems, and modeling of human behavior under risk.
Sessions
This talk will delve into the technical and conceptual challenges associated with drift detection on irregular time series exhibiting non-uniform seasonal patterns, such as end-of-month, pay-day, or holiday effects. We will demonstrate how drifts can be efficiently identified using a combination of the MIST (Multiple Irregular Seasonalities and Trend decomposition) and DTW (Dynamic Time Warping) algorithms.