רוברט מושקוביץ'

Predicting an event of interest based on heterogeneous multivariate temporal data is challenging but desirable as it allows the utilization of all types of temporal variables. In various domains, symbolic time intervals (STIs) can be used to represent real-life events that vary in duration, such as the period a traffic light remains green, or the time a patient undergoes treatment or is on medication. Further, heterogeneous multivariate temporal data may be composed of STIs along with event-driven or continuous temporal variables, such as traffic collisions or blood test values. Temporal abstraction can be used to uniformly represent heterogeneous multivariate temporal variables with STIs, from which frequent time intervals related patterns (TIRPs) can be discovered. We extend earlier work on continuous completion prediction of a single TIRP that ends with an event of interest, introducing a continuous prediction method based on multiple different instances of multiple TIRPs that end with the event of interest, for which we propose and evaluate several weighted aggregation functions. The proposed method overall performed better on real-life, medical, and non-medical datasets, than the use of a single TIRP, and in comparison to the baseline models (XGBoost, ResNet, LSTM-FCN, and ROCKET).