Periodic Pattern Analysis in Time Series Databases

Abstract

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.

Scaling-invariant boundary image matching using time-series matching techniques

“…Algorithm 1 shows a recursive algorithm that uses the divide-and-conquer strategy. 3 Brief explanation on the algorithm is as follows. In Lines 1 and 2 we first compute the lower and upper bounds of the scaling-invariant distance with the whole x and y scaling ranges.…”

“…Hereafter, we use time-series and sequences interchangeably. Finding data sequences similar to the given query sequence from the database is called time-series matching [2,3,14,16,21,32]. In this paper we perform time-series matching under the Euclidean distance-based similarity model [2,10,31].…”

“…Owing to advances in storage and computing power, there have been many research efforts on time-series matching to exploit large time-series databases [2,3,14,19,33,38]. In addition, there have been several recent attempts to apply these time-series matching techniques to practical applications such as handwritten recognition [30], image matching [20,22,41], query by humming [47], trajectory mining [9], and biological sequence matching [25].…”

TiP: Analyzing Periodic Time Series Patterns

“…Time Series Representation A time series X can be split into a sequence of subsequences of fixed length by adding an additional time domain [1]. This yields a dual-domain representation having a 3D surface.…”

“…TiP utilizes the dual-domain representation of time series and the thresholdbased approach [1] to extract periodic patterns from them. Beyond the interest of [2], the temporal location and the evolution of consecutive patterns are focused.…”