Self-organizing time map: An abstraction of temporal multivariate patterns

Peter Sarlin*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

37 Citations (Scopus)


This paper adopts and adapts Kohonen's standard self-organizing map (SOM) for exploratory temporal structure analysis. The self-organizing time map (SOTM) implements SOM-type learning to one-dimensional arrays for individual time units, preserves the orientation with short-term memory and arranges the arrays in an ascending order of time. The two-dimensional representation of the SOTM attempts thus twofold topology preservation, where the horizontal direction preserves time topology and the vertical direction data topology. This enables discovering the occurrence and exploring the properties of temporal structural changes in data. For representing qualities and properties of SOTMs, we adapt measures and visualizations from the standard SOM paradigm, as well as introduce a measure of temporal structural changes. The functioning of the SOTM, and its visualizations and quality and property measures, are illustrated on artificial toy data. The usefulness of the SOTM in a real-world setting is shown on poverty, welfare and development indicators.

Original languageEnglish
Peer-reviewed scientific journalNeurocomputing
Pages (from-to)496-508
Number of pages13
Publication statusPublished - 01.01.2013
MoE publication typeA1 Journal article - refereed


  • 511 Economics
  • Dynamic visual clustering
  • Exploratory data analysis
  • Exploratory temporal structure analysis
  • Self-organizing map
  • Self-organizing time map


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