Temporal ensemble of multiple patterns’ instances for continuous prediction of events

In real-life data of various domains, such as traffic, meteorology, or healthcare data, events may have varying durations. Moreover, heterogeneous multivariate temporal data may consist of varying samplings, including regular sampling in different frequencies or irregular, as well as events data of different types, having fixed or varying duration. We propose to uniformly represent heterogeneous multivariate temporal data using symbolic time-intervals, from which a model that predicts an occurrence of events early can be learned. We introduce a novel use of time-interval-related patterns (TIRPs), in which patterns that end with an event of interest can be used to continuously estimate the event’s occurrence probability in real-time. Recently, we introduced a model that allows continuous prediction of the completion of a pattern, which is extended in this work, to also predict the expected completion time. This work focuses on predicting the probability and time occurrence of an event based on multiple different instances of patterns that end with the event, for which we propose and evaluate aggregation functions. A rigorous evaluation was conducted on four real-life datasets to assess the effectiveness of the proposed model and the aggregation functions. The proposed model performed better than the baseline models (ResNet, LSTM-FCN, ROCKET, and XGBoost) for all datasets.