Periodicity detection in time series databases

Elfeky, Mohamed; Aref, Walid G.; Elmagarmid, Ahmed K.

doi:10.1109/tkde.2005.114

Cited by 190 publications

(158 citation statements)

References 19 publications

(29 reference statements)

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“…"A time series Y is said to be periodic with a period p if it can be divided into equal length segments, each of length p, that are almost similar" [4]. One simple instance is a time series Y={abcabcabcabd} which is obviously periodic with ℓ =3.…”

Section: Periodicity Detection Problemmentioning

confidence: 99%

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Periodicity Estimation in Mechanical Acoustic Time-Series Data

Zhu

Hong

2015

MATEC Web of Conferences

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Abstract. Periodicity estimation in mechanical acoustic time-series data is a well-established problem in data mining as it can be applicable in variety of disciplines either for anomaly detection or for prediction purposes in industry. In this paper, we develop a new approach for capturing and characterizing periodic patterns in time-series data by virtue of the dynamic time warping (DTW). We have conducted extensive experiments to evaluate the proposed approach with synthetic data and our collected data in practice. Experimental results demonstrated its effectiveness and robustness on periodicity detection in highly noised data.

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Section: Periodicity Detection Problemmentioning

confidence: 99%

“…Since the resulted comparison cost can measure the compatibility of candidate segments, specifically, a lower cost indicates a more compatibility for one compared segment, we can determine the expected period-length ℓ by seeking the minimum comparison cost, as: ℓ←l *, where l *= i i C min arc (4) …”

Section: Periodicity Determinationmentioning

confidence: 99%

Periodicity Estimation in Mechanical Acoustic Time-Series Data

Zhu

Hong

2015

MATEC Web of Conferences

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“…For this purpose, many different algorithms and methods have been introduced to perform pattern, e.g., [2,5,7,[17][18][19] and periodicity detection, e.g., [12,[20][21][22]. Moreover, the past decades have witnessed the utilization of two powerful data structures for time series analysis, namely suffix trees and suffix arrays.…”

Section: Related Workmentioning

confidence: 99%

“…A suffix tree is a representation of all suffixes of a string in a tree format [13]; it is considered a very powerful data structure [20,21,23]. Suffix trees are heavily used for data mining purposes in many scientific and commercial fields (e.g., financial, marketing, frequent item-sets detection, DNA) due to their advantages in string processing [12,21,22,24].…”

Section: Related Workmentioning

confidence: 99%

Analyzing very large time series using suffix arrays

2014

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Suffix arrays form a powerful data structure for pattern detection and matching. In a previous work, we presented a novel algorithm (COV) which is the only algorithm that allows the detection of all repeated patterns in a time series by using the actual suffix array. However, the requirements for storing the actual suffix strings even on external media makes the use of suffix arrays impossible for very large time series. We have already proved that using the concept of Longest Expected Repeated Pattern (LERP) allows the actual suffices to be stored in linear capacity O(n) on external media. The repeated pattern detection using LERP has analogous time complexity, and thus makes the analysis of large time series feasible and limited only to the size Further, when empirical knowledge related to the distribution of repeated pattern's length is available, the proposed method (MLERP) can achieve better time performance compared to the standard LERP method and definitely much better than using any other pattern matching algorithm and applying brute force techniques which are unfeasible in logical (human) time frame. Thus, we may argue that MLERP is a very useful tool for detecting all repeated patterns in a time series regardless of its size and hardware limitations.

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“…Periodic pattern mining problems for time-series data can be categorized into two types; (i) full periodic patterns mining [3], [8], where every point in time contributes to the cyclic behavior of the time-series and (ii) partial periodic patterns mining [4], [10], the more general type, which specifies the behavior of the time-series at some point but not at all points in the time-series. However, although periodic pattern mining is closely related to our work, it cannot be directly applied in the discovery of regular patterns from a transactional database because it considers time-series data.…”

Section: Related Workmentioning

confidence: 99%

Mining Regular Patterns in Transactional Databases

Tanbeer

Ahmed

Jeong

et al. 2008

IEICE Transactions on Information and Systems

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SUMMARYThe frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a transactional database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from transactional databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.

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Periodicity detection in time series databases

Cited by 190 publications

References 19 publications

Periodicity Estimation in Mechanical Acoustic Time-Series Data

Periodicity Estimation in Mechanical Acoustic Time-Series Data

Analyzing very large time series using suffix arrays

Mining Regular Patterns in Transactional Databases

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