Measuring similarity of univariate time series

Kljun, Maša (author), Štrumbelj, Erik (mentor)

In this thesis we review 12 time series similarity measures and 3 classifications of these measures into groups. We view similarity measures in terms of time complexity, support of time series of different lengths, and normalization. With empirical evaluation we check measures' invariances to warping and scaling, their clustering performance, and how similar they are. We find out that although several measures perform well on average no measure performs well in all cases. We see that the Piccolo distance is invariant to warping and scaling, and that it stands out with its clustering performance and linear time complexity. We also see that compression-based measures perform poorly on average.

• Language: English
• Keywords: time series, similarity measures, classification of similarity measures, clustering
• Work type: Bachelor thesis/paper
• Typology: 2.11 - Undergraduate Thesis
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2020
• PID: 20.500.12556/RUL-119310
• COBISS.SI-ID: 28472067
• Publication date in RUL: 07.09.2020

Secondary language

• Language: Slovenian
• Title: Merjenje podobnosti univariatnih časovnih vrst

Abstract

V diplomskem delu obravnavamo 12 mer podobnosti za časovne vrste in 3 delitve le-teh v skupine. Mere podobnosti obravnavamo z vidika njihovih časovnih zahtevnosti ter drugih lastnosti, kot so sposobnost primerjave časovnih vrst različnih dolžin in normalizacija razdalje. Empirično preverimo invariantnost mer na ukrivljanje in množenje s skalarjem, njihovo uspešnost pri gručenju in kako podobne so si. Ugotovimo, da nobena mera ni ustrezna v vseh primerih, saj ima vsaka svoje pomanjkljivosti. Vidimo, da je razdalja Piccolo invariantna na ukrivljanje in množenje s skalarjem ter da izstopa s svojo linearno časovno zahtevnostjo in dobrim rezultatom pri gručenju. Vidimo tudi, da mere, ki temeljijo na kompresiji, v povprečju ne dajejo dobrih rezultatov.

• Keywords: časovne vrste, mere podobnosti, klasifikacija mer podobnosti, gručenje