Approximate Periodicity

Amir, Amihood; Eisenberg, Estrella; Levy, Avivit

doi:10.1007/978-3-642-17517-6_5

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…In [4] these questions are studied for the Hamming Distance, where strings are possibly corrupted with substitution errors, i.e., a character may be substituted by a different character, and for the Swap distance, where the errors are the exchange of two adjacent symbols (with no symbol participating in more than one exchange). Let S be a string of length n over alphabet Σ.…”

Section: Resultsmentioning

confidence: 99%

“…This fact is formalized by considering different metrics to define the error. In [4] approximate periodicity is studied under two metrics: the Hamming distance and the swap distance. It may also be interesting to get an approximation of the number of errors, assuming an approximate periodic nature, even if we do not know the period or the exact statistics of the corruptions.…”

Section: Approximating the Period Under String Metricsmentioning

confidence: 99%

“…Appropriate kangaroo jumps allow [4] to carefully collect statistics on the k-error periodicity under the Hamming distance. This enables finding the approximate period of S, P , in time O(nk log log n), where k is the number of hamming errors between S and S P .…”

Section: Finding the Approximate Period Under The Hamming Distancementioning

confidence: 99%

“…[4] provide a careful study of the properties of these internal swaps which allows finding the approximate period of S under the swap distance in time O(n 2 ).…”

Section: Finding the Approximate Period Under The Swap Distancementioning

confidence: 99%

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Approximate Period Detection and Correction

Amir

Levy

2012

String Processing and Information Retrieval

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Abstract. Periodicity has been historically well studied and has numerous applications. In nature, however, few cyclic phenomena have an exact period.This paper surveys some recent results in approximate periodicity: concept definition, discovery or recovery, techniques and efficient algorithms. We will also show some interesting connections between error correction codes and periodicity.We will try to pinpoint the issues involved, the context in the literature, and possible future research directions.

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Section: Resultsmentioning

confidence: 99%

Section: Approximating the Period Under String Metricsmentioning

confidence: 99%

Section: Finding the Approximate Period Under The Hamming Distancementioning

confidence: 99%

“…[4] provide a careful study of the properties of these internal swaps which allows finding the approximate period of S under the swap distance in time O(n 2 ).…”

Section: Finding the Approximate Period Under The Swap Distancementioning

confidence: 99%

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Approximate Period Detection and Correction

Amir

Levy

2012

String Processing and Information Retrieval

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“…Existing periodicity detection methods can be categorized based on a number of factors [1,5,6,7,8,9,10,11,12,13,14,16,24,25]. These factors include parameter dependency, the type of periodicity detected, the span of periodicity detected and the domain in which the periodicity is detected.…”

Section: Related Workmentioning

confidence: 99%

MBPD: Motif-Based Period Detection

Otunba

Lin

Senin

2014

Lecture Notes in Computer Science

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Abstract. Massive amounts of data are generated daily at a rapid rate. As a result, the world is faced with unprecedented challenges and opportunities on managing the ever-growing data. These challenges are prevalent in time series for obvious reasons. Clearly, there is an urgent need for efficient solutions to mine large-scale time series databases. One of such data mining tasks is periodicity mining. Efficient and effective periodicity mining techniques in big data would be useful in cases such as finding animal migration patterns, analysis of stock market data for periodicity, and outlier detection in electrocardiogram (ECG), analyses of periodic disease outbreak etc. This work utilizes the notion of time series motifs for approximate period detection. Specifically, we present a novel and simple method to detect periods on time series data based on recurrent patterns. Our approach is effective, noiseresilient, and efficient. Experimental results show that our approach is superior compared to a popularly used period detection technique with respect to accuracy while requiring much less time and space.

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