scholarly journals Change Point Detection in Terrorism-Related Online Content Using Deep Learning Derived Indicators

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 274
Author(s):  
Ourania Theodosiadou ◽  
Kyriaki Pantelidou ◽  
Nikolaos Bastas ◽  
Despoina Chatzakou ◽  
Theodora Tsikrika ◽  
...  
Keyword(s):  
Time Series ◽  
Change Point ◽  
Hate Speech ◽  
Change Point Detection ◽  
Change Points ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Depth Analysis ◽  
Textual Data ◽  
Point Detection

Given the increasing occurrence of deviant activities in online platforms, it is of paramount importance to develop methods and tools that allow in-depth analysis and understanding to then develop effective countermeasures. This work proposes a framework towards detecting statistically significant change points in terrorism-related time series, which may indicate the occurrence of events to be paid attention to. These change points may reflect changes in the attitude towards and/or engagement with terrorism-related activities and events, possibly signifying, for instance, an escalation in the radicalization process. In particular, the proposed framework involves: (i) classification of online textual data as terrorism- and hate speech-related, which can be considered as indicators of a potential criminal or terrorist activity; and (ii) change point analysis in the time series generated by these data. The use of change point detection (CPD) algorithms in the produced time series of the aforementioned indicators—either in a univariate or two-dimensional case—can lead to the estimation of statistically significant changes in their structural behavior at certain time locations. To evaluate the proposed framework, we apply it on a publicly available dataset related to jihadist forums. Finally, topic detection on the estimated change points is implemented to further assess its effectiveness.

scholarly journals Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Locally adaptive change-point detection (LACPD) with applications to environmental changes

2021 ◽  
Author(s):  
Mehdi Moradi ◽  
Manuel Montesino-SanMartin ◽  
M. Dolores Ugarte ◽  
Ana F. Militino
Keyword(s):  
Time Series ◽  
Change Point ◽  
Error Probability ◽  
Type I Error ◽  
Change Point Detection ◽  
Alternative Methods ◽  
Change Points ◽  
Type I ◽  
Type I Error Probability ◽  
Point Detection

AbstractWe propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set of time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate for the lack of enough data near the time series’ tails. Through a simulation study, we analyse its behaviour in the presence of an early/middle/late change-point in the mean, and compare its performance with some of the frequently used and recently developed change-point detection methods in terms of power, type I error probability, area under the ROC curves (AUC), absolute bias, variance, and root-mean-square error (RMSE). We conclude that LACPD outperforms other methods by maintaining a low type I error probability. Unlike some other methods, the performance of LACPD does not depend on the time index of change-points, and it generally has lower bias than other alternative methods. Moreover, in terms of variance and RMSE, it outperforms other methods when change-points are close to the time series’ tails, whereas it shows a similar (sometimes slightly poorer) performance as other methods when change-points are close to the middle of time series. Finally, we apply our proposal to two sets of real data: the well-known example of annual flow of the Nile river in Awsan, Egypt, from 1871 to 1970, and a novel remote sensing data application consisting of a 34-year time-series of satellite images of the Normalised Difference Vegetation Index in Wadi As-Sirham valley, Saudi Arabia, from 1986 to 2019. We conclude that LACPD shows a good performance in detecting the presence of a change as well as the time and magnitude of change in real conditions.

scholarly journals Change-Point Detection of Peak Tibial Acceleration in Overground Running Retraining

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1720
Author(s):  
Pieter Van den Berghe ◽  
Maxim Gosseries ◽  
Joeri Gerlo ◽  
Matthieu Lenoir ◽  
Marc Leman ◽  
...  
Keyword(s):  
Real Time ◽  
Change Point ◽  
Major Change ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Point Analysis ◽  
Biofeedback System ◽  
Individualized Approach ◽  
Tibial Acceleration ◽  
Point Detection

A method is presented for detecting changes in the axial peak tibial acceleration while adapting to self-discovered lower-impact running. Ten runners with high peak tibial acceleration were equipped with a wearable auditory biofeedback system. They ran on an athletic track without and with real-time auditory biofeedback at the instructed speed of 3.2 m·s−1. Because inter-subject variation may underline the importance of individualized retraining, a change-point analysis was used for each subject. The tuned change-point application detected major and subtle changes in the time series. No changes were found in the no-biofeedback condition. In the biofeedback condition, a first change in the axial peak tibial acceleration occurred on average after 309 running gait cycles (3′40″). The major change was a mean reduction of 2.45 g which occurred after 699 running gait cycles (8′04″) in this group. The time needed to achieve the major reduction varied considerably between subjects. Because of the individualized approach to gait retraining and its relatively quick response due to a strong sensorimotor coupling, we want to highlight the potential of a stand-alone biofeedback system that provides real-time, continuous, and auditory feedback in response to the axial peak tibial acceleration for lower-impact running.

scholarly journals Using Graph Spectral to solve Change Point Detection Problems

2018 ◽  
Author(s):  
Luis Gustavo C. Uzai ◽  
André Y. Kashiwabara
Keyword(s):  
Time Series ◽  
Change Point ◽  
State Of The Art ◽  
The State ◽  
Change Point Detection ◽  
Change Points ◽  
Graph Spectrum ◽  
Point Detection ◽  
Over Time

Time series are sequence of values distributed over time. Analyzing time series is important in many areas including medical, financial, aerospace, commercial and entertainment. Change Point Detection is the problem of identifying changes in meaning or distribution of data in a time series. This article presents Spec, a new algorithm that uses the graph spectrum to detect change points. The Spec was evaluated using the UCR Archive which is a large da- tabase of different time series. Spec performance was compared to the PELT, ECP, EDM, and gSeg algorithms. The results showed that Spec achieved a better accuracy compared to the state of the art in some specific scenarios and as efficient as in most cases evaluated.

Change Point Detection For Clustered Expression Data In Short Time Series.

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Change Point Detection ◽  
Change Points ◽  
Point Detection ◽  
Over Time ◽  
Time Point

Abstract Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals.Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question.Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.

A Novel Change-Point Detection Approach for Monitoring High-Dimensional Traffics in Distributed Systems

2014 ◽  
Vol 536-537 ◽  
pp. 499-511 ◽  
Author(s):  
Li Zhao ◽  
Qian Liu ◽  
Peng Du ◽  
Ge Fu ◽  
Wei Cao
Keyword(s):  
Time Series ◽  
Distributed Systems ◽  
Change Point ◽  
Principal Component ◽  
Change Point Detection ◽  
Change Points ◽  
Clustering Methods ◽  
Detection Approach ◽  
Time Series Segmentation ◽  
Point Detection

Change-point detection is the problem of finding abrupt changes in time-series. However, the meaningful changes are usually difficult to identify from the original massive traffics, due to high dimension and strong periodicity. In this paper, we propose a novel change-point detection approach, which simultaneously detects change points from all dimensions of the traffics with three steps. We first reduce the dimensions by the classical Principal Component Analysis (PCA), then we apply an extended time-series segmentation method to detect the nontrivial change times, finally we identify the responsible applications for the changes by F-test. We demonstrate through experiments on datasets collected from four distributed systems with 44 applications that the proposed approach can effectively detect the nontrivial change points from the multivariate and periodical traffics. Our approach is more appropriate for mining the nontrivial changes in traffic data comparing with other clustering methods, such as center-based Kmeans and density-based DBSCAN.

CPMAN: Change Point Detection Approach in Time Series Based on the Prediction of Multi-stage Attention Networks

2021 ◽  
Vol 30 (05) ◽  
pp. 2150026
Author(s):  
Haizhou Du ◽  
Ziyi Duan ◽  
Yang Zheng
Keyword(s):  
Time Series ◽  
Change Point ◽  
Early Stage ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Attention Networks ◽  
Multi Stage ◽  
Detection Approach ◽  
Point Detection

Time series change point detection can identify the locations of abrupt points in many dynamic processes. It can help us to find anomaly data in an early stage. At the same time, detecting change points for long, periodic, and multiple input series data has received a lot of attention recently, and is universally applicable in many fields including power, environment, finance, and medicine. However, the performance of classical methods typically scales poorly for such time series. In this paper, we propose CPMAN, a novel prediction-based change point detection approach via multi-stage attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ the multi-stage attention-based networks and integrate the multi-series fusion mechanism. This module can adaptively extract features from the relevant input series and capture the long-term temporal dependencies. Secondly, in the change point detection module, we use the wavelet analysis-based algorithm to detect change points efficiently and identify the change points and outliers. Extensive experiments are conducted on various real-world datasets and synthetic datasets, proving the superiority and effectiveness of CPMAN. Our approach outperforms the state-of-the-art methods by up to 12.1% on the F1 Score.

scholarly journals Examination of Changes in Flood Data in Australia

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1734 ◽  
Author(s):  
Elias Ishak ◽  
Ataur Rahman
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Significance Level ◽  
Monotonic Trend ◽  
Point Analysis ◽  
Eastern Australia ◽  
Change In The Mean ◽  
The Mean ◽  
Point Detection

This study performs a simultaneous evaluation of gradual and abrupt changes in Australian annual maximum (AM) flood data using a modified Mann–Kendall and Pettitt change-point detection test. The results show that AM flood data in eastern Australia is dominated by downward trends. Depending on the significance level and study period under consideration, about 8% to 33% of stations are characterised by significant trends, where over 85% of detected significant trends are downward. Furthermore, the change-point analysis shows that the percentages of stations experiencing one abrupt change in the mean or in the direction of the trend are in the range of 8% to 33%, of which over 50% occurred in 1991, with a mode in 1995. Prominent resemblance between the monotonic trend and change-point analysis results is also noticed, in which a negative shift in the mean is observed at catchments that exhibited downward trends, and a positive shift in the mean is observed in the case of upward trends. Trend analysis of the segmented AM flood series based on their corresponding date indicates an absence of a significant trend, which may be attributed to the false detection of trends when the AM flood data are characterised by a shift in its mean.

scholarly journals Examining nonstationarity in the recruitment dynamics of fishes using Bayesian change point analysis

2017 ◽  
Vol 74 (5) ◽  
pp. 751-765 ◽  
Author(s):  
Tommi A. Perälä ◽  
Douglas P. Swain ◽  
Anna Kuparinen
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Change Point Analysis ◽  
Model Parameters ◽  
Point Analysis ◽  
Stock Recruitment ◽  
Novel Method ◽  
Point Detection ◽  
Time Invariant

Marine ecosystems can undergo regime shifts, which result in nonstationarity in the dynamics of the fish populations inhabiting them. The assumption of time-invariant parameters in stock–recruitment models can lead to severe errors when forecasting renewal ability of stocks that experience shifts in their recruitment dynamics. We present a novel method for fitting stock–recruitment models using the Bayesian online change point detection algorithm, which is able to cope with sudden changes in the model parameters. We validate our method using simulations and apply it to empirical data of four demersal fishes in the southern Gulf of St. Lawrence. We show that all of the stocks have experienced shifts in their recruitment dynamics that cannot be captured by a model that assumes time-invariant parameters. The detected shifts in the recruitment dynamics result in clearly different parameter distributions and recruitment predictions between the regimes. This study illustrates how stock–recruitment relationships can experience shifts, which, if not accounted for, can lead to false predictions about a stock’s recovery ability and resilience to fishing.

scholarly journals Retrospective Change-Points Detection for Multidimensional Time Series of Arbitrary Nature: Model-Free Technology Based on the ϵ-Complexity Theory

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1626
Author(s):  
Alexandra Piryatinska ◽  
Boris Darkhovsky
Keyword(s):  
Time Series ◽  
Change Point ◽  
Change Point Detection ◽  
Change Points ◽  
Detection Problem ◽  
Model Free ◽  
Multidimensional Time Series ◽  
Simulation Results ◽  
Point Detection ◽  
Present Simulation

We consider a retrospective change-point detection problem for multidimensional time series of arbitrary nature (in particular, panel data). Change-points are the moments at which the changes in generating mechanism occur. Our method is based on the new theory of ϵ-complexity of individual continuous vector functions and is model-free. We present simulation results confirming the effectiveness of the method.

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