Udo leads Applied Artificial Intelligence and Modeling Research and Development at SAS. With a 25-year track record of fostering technology innovation and excellence, Udo heads a team of expert developers and data scientists dedicated to pioneering cutting-edge software and leveraging advanced models to transform the way the world works. By imagining and building the next generation of AI-driven models and software solutions, Udo helps organizations harness the power of data and analytics to solve their toughest business challenges and outpace the world around them. Udo’s commitment to pushing the boundaries of what AI can achieve has earned him recognition as a global thought leader shaping the future of technology. In addition, Udo has written three publications and holds four patents in advanced analytics.
My esteemed colleague and recently-published author Jared Dean shared some thoughts on how ensemble models help make better predictions. For predictive modeling, Jared explains the value of two main forms of ensembles --bagging and boosting. It should not be surprising that the idea of combining predictions from more than one
We all have some sort of intuitive idea of what time series data is – it’s a bunch of measurements or observations that are marked by a time stamp – we know when the measurement was taken, as well as what was measured. This natural temporal ordering of the data
