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 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 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 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.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

scholarly journals Understanding the CoVID-19 pandemic Curve through statistical approach

2020 ◽  
Author(s):  
Ibrar ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

AbstractCurrent research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 – 9.89 and 0 – 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of −78.8 to 49.0%.

scholarly journals Change-point detection and trend analysis in monthly, seasonal and annual air temperature and precipitation series in Bartın province in the western Black Sea region of Turkey

2021 ◽  
Vol 46 (3) ◽  
pp. 223
Author(s):  
Barbaros Yaman ◽  
Mertol Ertuğrul
Keyword(s):  
Trend Analysis ◽  
Black Sea ◽  
Change Point ◽  
Change Point Detection ◽  
Significance Level ◽  
Black Sea Region ◽  
Sen’S Slope ◽  
Air Temperatures ◽  
Point Detection ◽  
Increasing Trends

Studies associated with climate change and variability are of great importance at both the global and local scale in the global climate crisis. In this study, change-point detection and trend analysis were carried out on mean, maximum, minimum air temperatures and total precipitation based on monthly, seasonal and annual scale in Bartın province located in the western Black Sea Region of Turkey. For this aim, 4-different homogenei-ty tests (von Neumann test, Pettitt test, Buishand range test and standard normal homogeneity test) for change-point detection, Modified Mann–Kendall test and Şen’s innovative trend test for trend analysis, and Sen’s slope test for the magnitude estimation of trends were used. According to the test results, the summer temperatures in particular show increasing trends at the 0.001 significance level. Mean maximum temperature in August, mean minimum temperature in June and August, and mean temperature in July and August are in increasing trend at the 0.001 significance level. Over a 51 year period (1965–2015) in Bartın province, the highest rate of change per decade in air temperatures is in August (0.55°C for Tmax, 0.46°C for Tmin and 0.43°C for Tmean) based on Sen’s slope. However, the study showed that apart from October precipitation, there is no significant trend in monthly, seasonal and annual precipitation in Bartın. Increasing trends in mentioned climate variables are also visually very clear and strong in Şen’s innovative trend method, and they comply with the statistical results. As a result, the study revealed some evidence that temperatures will increase in the future in Bartın and its environs.

scholarly journals Detection of multiple changes in mean by sparse parameter estimation

2013 ◽  
Vol 18 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Jirí Neubauer ◽  
Vítezslav Veselý
Keyword(s):  
Stochastic Process ◽  
Parameter Estimation ◽  
Standard Deviation ◽  
White Noise ◽  
Change Point ◽  
Change Point Detection ◽  
Change Points ◽  
Empirical Probability ◽  
The Mean ◽  
Point Detection

The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.

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