TY - GEN
T1 - Early Multiple Temporal Patterns Based Event Prediction in Heterogeneous Multivariate Temporal Data
AU - Itzhak, Nevo
AU - Jaroszewicz, Szymon
AU - Moskovitch, Robert
N1 - Publisher Copyright: Copyright © 2024 by SIAM.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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).
AB - 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).
KW - event prediction
KW - real-time prediction
KW - temporal abstraction
KW - temporal patterns
KW - time intervals
UR - http://www.scopus.com/inward/record.url?scp=85193496547&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
SP - 199
EP - 207
BT - Proceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
A2 - Shekhar, Shashi
A2 - Papalexakis, Vagelis
A2 - Gao, Jing
A2 - Jiang, Zhe
A2 - Riondato, Matteo
PB - Society for Industrial and Applied Mathematics Publications
T2 - 2024 SIAM International Conference on Data Mining, SDM 2024
Y2 - 18 April 2024 through 20 April 2024
ER -