Periodic Pattern Analysis in Time Series Databases

Abstract

Similarity search in time series data is used in diverse domains. The most prominent work has focused on similarity search considering either complete time series or certain subsequences of time series. Often, time series like temperature measurements consist of periodic patterns, i.e. patterns that repeatedly occur in defined periods over time. For example, the behavior of the temperature within one day is commonly correlated to that of the next day. Analysis of changes within the patterns and over consecutive patterns could be very valuable for many application domains, in particular finance, medicine, meteorology and ecology. In this paper, we present a framework that provides similarity search in time series databases regarding specific periodic patterns. In particular, an efficient threshold-based similarity search method is applied that is invariant against small distortions in time. Experiments on real-world data show that our novel similarity measure is more meaningful than established measures for many applications.