Early Multiple Temporal Patterns Based Event Prediction in Heterogeneous Multivariate Temporal Data

Nevo Itzhak, Szymon Jaroszewicz, Robert Moskovitch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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).

Original languageAmerican English
Title of host publicationProceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024
EditorsShashi Shekhar, Vagelis Papalexakis, Jing Gao, Zhe Jiang, Matteo Riondato
PublisherSociety for Industrial and Applied Mathematics Publications
Pages199-207
Number of pages9
ISBN (Electronic)9781611978032
StatePublished - 1 Jan 2024
Event2024 SIAM International Conference on Data Mining, SDM 2024 - Houston, United States
Duration: 18 Apr 202420 Apr 2024

Publication series

NameProceedings of the 2024 SIAM International Conference on Data Mining, SDM 2024

Conference

Conference2024 SIAM International Conference on Data Mining, SDM 2024
Country/TerritoryUnited States
CityHouston
Period18/04/2420/04/24

Keywords

  • event prediction
  • real-time prediction
  • temporal abstraction
  • temporal patterns
  • time intervals

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

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