Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation

Albertetti, Fabrizio; Grossrieder, Lionel; Ribaux, Olivier; Stoffel, Kilian

doi:10.1016/j.asoc.2015.12.004

Cited by 18 publications

(8 citation statements)

References 39 publications

(54 reference statements)

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“…Time series segmentation has been studied and applied in many applications, such as security and machine fault detection as well. The time series segmentation is the decomposition process of series into homogeneous groups with similar characteristics.…”

Section: Methodsmentioning

confidence: 99%

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Atamuradov

Medjaher

Camci

et al. 2019

Quality & Reliability Eng

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In this paper, we propose an offline and online machine health assessment (MHA) methodology composed of feature extraction and selection, segmentation‐based fault severity evaluation, and classification steps. In the offline phase, the best representative feature of degradation is selected by a new filter‐based feature selection approach. The selected feature is further segmented by utilizing the bottom‐up time series segmentation to discriminate machine health states, ie, degradation levels. Then, the health state fault severity is extracted by a proposed segment evaluation approach based on within segment rate‐of‐change (RoC) and coefficient of variation (CV) statistics. To train supervised classifiers, a priori knowledge about the availability of the labeled data set is needed. To overcome this limitation, the health state fault‐severity information is used to label (eg, healthy, minor, medium, and severe) unlabeled raw condition monitoring (CM) data. In the online phase, the fault‐severity classification is carried out by kernel‐based support vector machine (SVM) classifier. Next to SVM, the k‐nearest neighbor (KNN) is also used in comparative analysis on the fault severity classification problem. Supervised classifiers are trained in the offline phase and tested in the online phase. Unlike to traditional supervised approaches, this proposed method does not require any a priori knowledge about the availability of the labeled data set. The proposed methodology is validated on infield point machine sliding‐chair degradation data to illustrate its effectiveness and applicability. The results show that the time series segmentation‐based failure severity detection and SVM‐based classification are promising.

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Section: Methodsmentioning

confidence: 99%

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Atamuradov

Medjaher

Camci

et al. 2019

Quality & Reliability Eng

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show abstract

“…Time series segmentation consists in dividing the original time series in segments with similar behavior according to some criteria and it can be either a preprocessing step in order to represent the time series more efficiently or a data mining technique on its own, able to extract information about the dynamics of the underlying phenomenon [1,2]. Segmentation methods have been utilized to analyze time series of diverse backgrounds, including biological, climate, remote sensing and crime-related data [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of time series in up- and down-trends using the epsilon-tau procedure with application to USD/JPY foreign exchange market data

Sousa

Takayasu

2020

PLoS ONE

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We propose the epsilon-tau procedure to determine up-and down-trends in a time series, working as a tool for its segmentation. The method denomination reflects the use of a tolerance level ε for the series values and a patience level τ in the time axis to delimit the trends. We first illustrate the procedure in discrete random walks, deriving the exact probability distributions of trend lengths and trend amplitudes, and then apply it to segment and analyze the trends of U.S. dollar (USD)/Japanese yen (JPY) market time series from 2015 to 2018. Besides studying the statistics of trend lengths and amplitudes, we investigate the internal structure of the trends by grouping trends with similar shapes and selecting clusters of shapes that rarely occur in the randomized data. Particularly, we identify a set of downtrends presenting similar sharp appreciation of the yen that are associated with exceptional events such as the Brexit Referendum in 2016.

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“…Change-point models are to fit piecewise linear regression models with unknown change points to a dataset whose distribution is suspected to change at change points. Change points models have been widely used in fields such as global surface temperature anomaly analysis [35] and crime analysis [36]. For the building energy disaggregation issue, change-point models are popular steady-state data-driven models, which disaggregate the temperature-dependent and temperature-independent energy consumptions based on piecewise linear regression on EvOT [33,34].…”

Section: Related Workmentioning

confidence: 99%

An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management

et al. 2017

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Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation

Cited by 18 publications

References 39 publications

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Feature selection and fault‐severity classification–based machine health assessment methodology for point machine sliding‐chair degradation

Segmentation of time series in up- and down-trends using the epsilon-tau procedure with application to USD/JPY foreign exchange market data

An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management

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