Constrained Motif Discovery in Time Series

Mohammad, Yasser; Nishida, Toyoaki

doi:10.1007/s00354-009-0068-x

Cited by 38 publications

(23 citation statements)

References 15 publications

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“…The proposed technique treats a statistic as a text document and extracts native segments from the time series as words. Yasser et al [7] described the problem of Constrained Motif Detectionusingtwo approaches. MC-Full and MC-Inc has been utilized for solving the constrained motif detection issues in a more effective way.…”

Section: IImentioning

confidence: 99%

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

Kaur¹

2017

IJRASET

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Section: IImentioning

confidence: 99%

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

Kaur¹

2017

IJRASET

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“…Landmarks can be used to break time series into meaningful segments, which are also referred to as key-points [8], breakpoints [47] and change-points [37]. Perng et al [43] propose a feature-based technique, which uses landmarks instead of the raw data for processing.…”

Section: Related Workmentioning

confidence: 99%

“…A considerable number of such methods have been proposed in the past, including Symbolic Aggregate approXimation (SAX) [29], bit-level approximation [7], and Piecewise Aggregate Approximation (PAA) [25]. 1 In this paper specifically, we focus on the representation of time series by means of landmarks [43] (also referred to as key-points [8], break-points [47] and change-points [37]), which can be thought of as those points in the time series that are obviously remarkable (peaks, valleys, inflection points, …). Rather than matching every detail of the data and the template, only the landmarks will be matched, and subsequent landmarks will be checked for their relationship to one another.…”

Section: Introductionmentioning

confidence: 99%

Predefined pattern detection in large time series

Miao

Vespier

Cachucho

et al. 2016

Information Sciences

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a b s t r a c tPredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept of trust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance.

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“…The motifs are discovered in linear time and constant memory costs using random sampling. However, this approach can lead to poor performance for long time series with infrequent motifs [13].…”

Section: Definition 5 Frequent K-motifsmentioning

confidence: 99%

Discovering Frequent Patterns on Agrometeorological Data with TrieMotif

Chino

Gonçalves

Romani

et al. 2015

Enterprise Information Systems

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Abstract. The "food safety" issue has concerned governments from several countries. The accurate monitoring of agriculture have become important specially due to climate change impacts. In this context, the development of new technologies for monitoring are crucial. Finding previously unknown patterns that frequently occur on time series, known as motifs, is a core task to mine the collected data. In this work we present a method that allows a fast and accurate time series motif discovery. From the experiments we can see that our approach is able to efficiently find motifs even when the size of the time series goes longer. We also evaluated our method using real data time series extracted from remote sensing images regarding sugarcane crops. Our proposed method was able to find relevant patterns, as sugarcane cycles and other land covers inside the same area, which are really useful for data analysis.

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Constrained Motif Discovery in Time Series

Cited by 38 publications

References 15 publications

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

Motif Detection Using Numerical Data Optimization Technique in ECG Signals

Predefined pattern detection in large time series

Discovering Frequent Patterns on Agrometeorological Data with TrieMotif

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