Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns

Abstract

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.

Cited by in corpus (5)

• The Effects of Character-Level Data Augmentation on Style-Based Dating of Historical Manuscripts
• Clustering evolving data using kernel-based methods
• iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans
• Regshock: Interactive Visual Analytics of Systemic Risk in Financial Networks
• Automated and Weighted Self-Organizing Time Maps