An efficient index structure for shift and scale invariant search of mufti-attribute time sequences

Kahveci, Tamer; Singh, Achhaibar; Gurel, Ahmet Volkan

doi:10.1109/icde.2002.994720

Cited by 8 publications

(8 citation statements)

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“…A shift invariant estimator is analogous to a time-invariant function; defined such that if z(x) = y(x), then z(x + t) = y(x + t) where t is the time shift (Kahvec et al 2001;Oppenheim and Schafer 1975). An estimator z(x) is said to be scale-invariant when multiplication of all elements of the sample (x 1 , ..., x n ) by an arbitrary nonnegative value β results in multiplication of the estimator by the same value (βz(x)).…”

Section: Estimator Propertiesmentioning

confidence: 99%

Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

AghaKouchak

Nasrollahi

2009

Water Resour Manage

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Extreme rainfall events and the clustering of extreme values provide fundamental information which can be used for the risk assessment of extreme floods. Event probability can be estimated using the extreme value index (γ ) which describes the behavior of the upper tail and measures the degree of extreme value clustering. In this paper, various semi-parametric and parametric extreme value index estimators are implemented in order to characterize the tail behavior of longterm daily rainfall time series. The results obtained from different estimators are then used to extrapolate the distribution function of extreme values. Extrapolation can be employed to estimate the occurrence probability of rainfall events above a given threshold. The results indicated that different estimators may result in considerable differences in extreme value index estimates. The uncertainty of the extreme value estimators is also investigated using the bootstrap technique. The analyses showed that the parametric methods are superior to the semi-parametric approaches. In particular, the likelihood and Two-Step estimators are preferred as they are found to be more robust and consistent for practical application.

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Section: Estimator Propertiesmentioning

confidence: 99%

Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

AghaKouchak

Nasrollahi

2009

Water Resour Manage

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“…They first projected these points to eliminating the effect of amplitude shifting, then determined the optimal scaling factors to obtain the minimum distance between two time series. Unfortunately, the measurement obtained by this method violates the symmetry property [10]. Dynamic time warping (DTW) is a robust measure for time series [12].…”

Section: Introductionmentioning

confidence: 99%

Efficient Time Series Clustering and Its Application to Social Network Mining

Zhou

Zhao

2014

Journal of Intelligent Systems

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Mining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

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“…Comparing time series is a tougher problem, for which many different approaches have been proposed in the literature, ranging from more classical approaches based on Fourier transforms [18] or wavelets [19], through methods that transform and approximate the time series to another series with which it is being compared [20], methods based on the identification of breakpoints in time series [21], methods that compare time series in terms of how many subsequences they have in common [22], etc. Most of these techniques work with whole time series.…”

Section: Comparing Two Complex Individualsmentioning

confidence: 99%

Generating Reference Models for Structurally Complex Data

Alonso¹,

Lara²,

Martínez³

et al. 2013

Methods Inf Med

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SummaryObjectives: We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of singlevalued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc. Methods: We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series. Results: The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basketball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%). Conclusions: We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.

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An efficient index structure for shift and scale invariant search of mufti-attribute time sequences

Cited by 8 publications

References 0 publications

Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data

Efficient Time Series Clustering and Its Application to Social Network Mining

Generating Reference Models for Structurally Complex Data

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