Maxim Nikiforov, Danila Gorkavchenko, Murad Mamedov, Andrey Novikov and Nikita Pushchin, Exactpro
The paper describes authors' experience of implementing machine learning techniques to predict deviations in service workflows duration, long before the post-trade system reports them as not completed on time. The prediction is based on analyzing a large set of performance metrics collected every second from modules of the system, and using regression models to detect running workflows that are likely to be hung. This article covers raw data preprocessing, dataset dimensionality reduction, applied regression models and their performance. Problems to be resolved and project roadmap are also described.