A likelihood ratio distance measure for the similarity between the Fourier transform of time series

GJ Janacek, AJ Bagnall, M Powell

Research output: Chapter in Book/Report/Conference proceedingChapter

17 Citations (Scopus)


Fast Fourier Transforms (FFTs) have been a popular transformation and compression technique in time series data mining since first being proposed for use in this context in [1]. The Euclidean distance between coefficients has been the most commonly used distance metric with FFTs. However, on many problems it is not the best measure of similarity available. In this paper we describe an alternative distance measure based on the likelihood ratio statistic to test the hypothesis of difference between series. We compare the new distance measure to Euclidean distance on five types of data with varying levels of compression. We show that the likelihood ratio measure is better at discriminating between series from different models and grouping series from the same model.
Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining
EditorsTu Bao Ho, David Cheung, Huan Liu
PublisherSpringer Berlin / Heidelberg
Number of pages9
Publication statusPublished - 2005
Event9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-05) -
Duration: 1 Jan 2005 → …

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg


Conference9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-05)
Period1/01/05 → …

Cite this