Classification Models for Partially Ordered Sequences

Stephanie Ger*, Diego Klabjan, Jean Utke

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Many models such as Long Short Term Memory (LSTMs), Gated Recurrent Units (GRUs) and transformers have been developed to classify time series data with the assumption that events in a sequence are ordered. On the other hand, fewer models have been developed for set based inputs, where order does not matter. There are several use cases where data is given as partially-ordered sequences because of the granularity or uncertainty of time stamps. We introduce a novel transformer based model for such prediction tasks, and benchmark against extensions of existing order invariant models. We also discuss how transition probabilities between events in a sequence can be used to improve model performance. We show that the transformer-based equal-time model outperforms extensions of existing set models on three data sets.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030863616
StatePublished - 2021
Event30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online
Duration: Sep 14 2021Sep 17 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12891 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Conference on Artificial Neural Networks, ICANN 2021
CityVirtual, Online


  • Recurrent Neural Networks
  • Timeseries
  • Transformers

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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