# Time series classification based on triadic time series motifs

## TL;DR

This paper introduces a novel triadic time series motif analysis method that effectively classifies various chaotic and real-world time series, outperforming traditional dynamic time warping in certain cases.

## Contribution

It defines six types of triadic motifs and demonstrates their effectiveness in classifying diverse time series datasets with high accuracy.

## Key findings

- Motif profiles can distinguish different chaotic systems.
- The method outperforms dynamic time warping on some datasets.
- Triadic motifs enhance time series classification accuracy.

## Abstract

It is of great significance to identify the characteristics of time series to qualify their similarity. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map. Based on the similarity of motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We further apply the motif analysis method to the UCR Time Series Classification Archive and provide evidence of good classification ability for some data sets. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain data sets investigated in this work.