The Influence of Global Constraints on Similarity Measures for Time-Series Databases

07/01/2011
by   Vladimir Kurbalija, et al.
0

A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents and important tool in many application areas, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, etc. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10-15 time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbor graphs. This observation explains the potential for accuracy gains when using constrained measures, highlighting the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy.

READ FULL TEXT

Authors

03/07/2019

Fast Exact Dynamic Time Warping on Run-Length Encoded Time Series

Dynamic Time Warping (DTW) is a well-known similarity measure for time s...
01/17/2021

Free congruence: an exploration of expanded similarity measures for time series data

Time series similarity measures are highly relevant in a wide range of e...
05/01/2020

Integrated Time Series Summarization and Prediction Algorithm and its Application to COVID-19 Data Mining

This paper proposes a simple method to extract from a set of multiple re...
08/29/2018

Elastic bands across the path: A new framework and methods to lower bound DTW

There has been renewed recent interest in developing effective lower bou...
02/14/2021

Tight lower bounds for Dynamic Time Warping

Dynamic Time Warping (DTW) is a popular similarity measure for aligning ...
05/19/2020

A reduction of the dynamic time warping distance to the longest increasing subsequence length

The similarity between a pair of time series, i.e., sequences of indexed...
10/11/2020

Early Abandoning PrunedDTW and its application to similarity search

The Dynamic Time Warping ("DTW") distance is widely used in time series ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.