  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.
