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Relevant bibliographies by topics / Change point and trend detection

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Academic literature on the topic 'Change point and trend detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Change point and trend detection"

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Militino, Ana, Mehdi Moradi, and M. Ugarte. "On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data." Remote Sensing 12, no. 6 (2020): 1008. http://dx.doi.org/10.3390/rs12061008.

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Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-pe

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Ishak, Elias, and Ataur Rahman. "Examination of Changes in Flood Data in Australia." Water 11, no. 8 (2019): 1734. http://dx.doi.org/10.3390/w11081734.

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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

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Alashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.002.

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Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as speci

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Alashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.02.

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Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as speci

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Wehbe, Youssef, and Marouane Temimi. "A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula." Remote Sensing 13, no. 2 (2021): 247. http://dx.doi.org/10.3390/rs13020247.

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A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the

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Vaman, H. J., and K. Suresh Chandra. "OPTIMAL CHANGE-POINT DETECTION IN TREND MODELS WITH INTEGRATED MOVING AVERAGE ERRORS." Sequential Analysis 21, no. 1-2 (2002): 99–107. http://dx.doi.org/10.1081/sqa-120004175.

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Ray, Litan Kumar, Narendra Kumar Goel, and Manohar Arora. "Trend analysis and change point detection of temperature over parts of India." Theoretical and Applied Climatology 138, no. 1-2 (2019): 153–67. http://dx.doi.org/10.1007/s00704-019-02819-7.

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Sherwood, Steven C. "Simultaneous Detection of Climate Change and Observing Biases in a Network with Incomplete Sampling." Journal of Climate 20, no. 15 (2007): 4047–62. http://dx.doi.org/10.1175/jcli4215.1.

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Abstract All instrumental climate records are affected by instrumentation changes and variations in sampling over time. While much attention has been paid to the problem of detecting “change points” in time series, little has been paid to the statistical properties of climate signals that result after adjusting (“homogenizing”) the data—or to the effects of the irregular sampling and serial correlation exhibited by real climate records. These issues were examined here by simulating multistation datasets. Simple homogenization methods, which remove apparent artifacts and then calculate trends,

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Alhathloul, Saleh H., Abdul A. Khan, and Ashok K. Mishra. "Trend analysis and change point detection of annual and seasonal horizontal visibility trends in Saudi Arabia." Theoretical and Applied Climatology 144, no. 1-2 (2021): 127–46. http://dx.doi.org/10.1007/s00704-021-03533-z.

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Nguyen, Khanh Ninh, Annarosa Quarello, Olivier Bock, and Emilie Lebarbier. "Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties." Atmosphere 12, no. 9 (2021): 1102. http://dx.doi.org/10.3390/atmos12091102.

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This study investigates the sensitivity of the GNSSseg segmentation method to change in: GNSS data processing method, length of time series (17 to 25 years), auxiliary data used in the integrated water vapor (IWV) conversion, and reference time series used in the segmentation (ERA-Interim versus ERA5). Two GNSS data sets (IGS repro1 and CODE REPRO2015), representative of the first and second IGS reprocessing, were compared. Significant differences were found in the number and positions of detected change-points due to different a priori ZHD models, antenna/radome calibrations, and mapping func

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Dissertations / Theses on the topic "Change point and trend detection"

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Petersson, David, and Emil Backman. "Change Point Detection and Kernel Ridge Regression for Trend Analysis on Financial Data." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230729.

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The investing market can be a cold ruthless place for the layman. In order to get the chance of making money in this business one must place countless hours on research, with many different parameters to handle in order to reach success. To reduce the risk, one must look to many different companies operating in multiple fields and industries. In other words, it can be a hard task to manage this feat. With modern technology, there is now lots of potential to handle this tedious analysis autonomously using machine learning and clever algorithms. With this approach, the amount of analyzes is only

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Gao, Zhenguo. "Variance Change Point Detection under A Smoothly-changing Mean Trend with Application to Liver Procurement." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/82351.

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Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario that the variance of data may make a significant jump while the mean of data changes in a smooth fashion. It is motivated by a liver procurement experiment with organ surface temperature monitoring. Blindly applying the existing change point analysis methods to the example can yield erratic change point estimates since the smoothly-changing mean violates the sudden-change assumption. In my dissertation, we pro

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Hedberg, Sofia. "Regional Quantification of Climatic and Anthropogenic Impacts on Streamflows in Sweden." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269824.

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The anthropogenic impact on earth’s systems has rapidly increased since the middle of the last century and today it is hard to find a stream that is not influenced by human activities. The understanding of causes to changes is an important knowledge for future water management and planning and of that reason climatic and anthropogenic impact on streamflow changes in Sweden were explored and quantified. In the first step trends and abrupt changes in annual streamflow were detected and verified with the non- parametric Mann-Kendall’s and Pettitt’s test, all performed as moving window tests. In t

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Jawa, Taghreed Mohammed. "Statistical methods of detecting change points for the trend of count data." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28854.

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In epidemiology, controlling infection is a crucial element. Since healthcare associated infections (HAIs) are correlated with increasing costs and mortality rates, effective healthcare interventions are required. Several healthcare interventions have been implemented in Scotland and subsequently Health Protection Scotland (HPS) reported a reduction in HAIs [HPS (2015b, 2016a)]. The aim of this thesis is to use statistical methods and change points analysis to detect the time when the rate of HAIs changed and determine which associated interventions may have impacted such rates. Change points

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Garreau, Damien. "Change-point detection and kernel methods." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE061/document.

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Dans cette thèse, nous nous intéressons à une méthode de détection des ruptures dans une suite d’observations appartenant à un ensemble muni d’un noyau semi-défini positif. Cette procédure est une version « à noyaux » d’une méthode des moindres carrés pénalisés. Notre principale contribution est de montrer que, pour tout noyau satisfaisant des hypothèses raisonnables, cette méthode fournit une segmentation proche de la véritable segmentation avec grande probabilité. Ce résultat est obtenu pour un noyau borné et une pénalité linéaire, ainsi qu’une autre pénalité venant de la sélection de modèle

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Niu, Yue S., Ning Hao, and Heping Zhang. "Multiple Change-Point Detection: A Selective Overview." INST MATHEMATICAL STATISTICS, 2016. http://hdl.handle.net/10150/622820.

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Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strate

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Yang, Ping. "Adaptive trend change detection and pattern recognition in physiological monitoring." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/8932.

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Advances in monitoring technology have resulted in the collection of a vast amount of data that exceeds the simultaneous surveillance capabilities of expert clinicians in the clinical environment. To facilitate the clinical decision-making process, this thesis solves two fundamental problems in physiological monitoring: signal estimation and trend-pattern recognition. The general approach is to transform changes in different trend features to nonzero level-shifts by calculating the model-based forecast residuals and then to apply a statistical test or Bayesian approach on the residuals to de

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Mei, Yajun Lorden Gary. "Asymptotically optimal methods for sequential change-point detection /." Diss., Pasadena, Calif. : California Institute of Technology, 2003. http://resolver.caltech.edu/CaltechETD:etd-05292003-133431.

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Geng, Jun. "Quickest Change-Point Detection with Sampling Right Constraints." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/440.

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The quickest change-point detection problems with sampling right constraints are considered. Specially, an observer sequentially takes observations from a random sequence, whose distribution will change at an unknown time. Based on the observation sequence, the observer wants to identify the change-point as quickly as possible. Unlike the classic quickest detection problem in which the observer can take an observation at each time slot, we impose a causal sampling right constraint to the observer. In particular, sampling rights are consumed when the observer takes an observation and are replen

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Schröder, Anna Louise. "Methods for change-point detection with additional interpretability." Thesis, London School of Economics and Political Science (University of London), 2016. http://etheses.lse.ac.uk/3421/.

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The main purpose of this dissertation is to introduce and critically assess some novel statistical methods for change-point detection that help better understand the nature of processes underlying observable time series. First, we advocate the use of change-point detection for local trend estimation in financial return data and propose a new approach developed to capture the oscillatory behaviour of financial returns around piecewise-constant trend functions. Core of the method is a data-adaptive hierarchically-ordered basis of Unbalanced Haar vectors which decomposes the piecewise-constant tr

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Books on the topic "Change point and trend detection"

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Olympia, Hadjiliadis, ed. Quickest detection. Cambridge University Press, 2009.

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Rahat, Gideon, and Ofer Kenig. A Cross-National Comparison of Party Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808008.003.0005.

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The chapter presents an integrative cross-national comparative analysis of party change in all twenty-six countries under study. It finds that, apart from often sharing the same decline trend, the various indicators of party change appear to be independent of one another. The indicators that refer to the various mediators and those that refer to voters point clearly to decline, while those that look at the party background of representatives stand out as prominent survivors of a major decline trend. Party decline is evident in almost all countries, but its levels vary. Many explanations for va

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Kennett, Douglas J., and David A. Hodell. AD 750–1100 Climate Change and Critical Transitions in Classic Maya Sociopolitical Networks. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199329199.003.0007.

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Multiple palaeoclimatic reconstructions point to a succession of major droughts in the Maya Lowlands between AD 750 and 1100 superimposed on a regional drying trend that itself was marked by considerable spatial and temporal variability. The longest and most severe regional droughts occurred between AD 800 and 900 and again between AD 1000 and 1100. Well-dated historical records carved on stone monuments from forty Classic Period civic-ceremonial centers reflect a dynamic sociopolitical landscape between AD 250 and 800 marked by a complex of antagonistic, diplomatic, lineage-based, and subordi

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Rahat, Gideon, and Ofer Kenig. From Party Politics to Personalized Politics? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808008.001.0001.

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The book examines two of the most prominent developments in contemporary democratic politics, party change and political personalization, and the relationship between them. It presents a broad-brush, cross-national comparison of these phenomena that covers around fifty years in twenty-six countries through the use of more than twenty indicators. It demonstrates that, behind a general trend of decline of political parties, there is much variance among countries. In some, party decline is moderate or even small, which may point to adaptation to the changing environments these parties operate in.

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Fensholt, Rasmus, Cheikh Mbow, Martin Brandt, and Kjeld Rasmussen. Desertification and Re-Greening of the Sahel. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.553.

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In the past 50 years, human activities and climatic variability have caused major environmental changes in the semi-arid Sahelian zone and desertification/degradation of arable lands is of major concern for livelihoods and food security. In the wake of the Sahel droughts in the early 1970s and 1980s, the UN focused on the problem of desertification by organizing the UN Conference on Desertification (UNCOD) in Nairobi in 1976. This fuelled a significant increase in the often alarmist popular accounts of desertification as well as scientific efforts in providing an understanding of the mechanism

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Kenyon, Ian R. Quantum 20/20. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198808350.001.0001.

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This text reviews fundametals and incorporates key themes of quantum physics. One theme contrasts boson condensation and fermion exclusivity. Bose–Einstein condensation is basic to superconductivity, superfluidity and gaseous BEC. Fermion exclusivity leads to compact stars and to atomic structure, and thence to the band structure of metals and semiconductors with applications in material science, modern optics and electronics. A second theme is that a wavefunction at a point, and in particular its phase is unique (ignoring a global phase change). If there are symmetries, conservation laws foll

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Book chapters on the topic "Change point and trend detection"

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Ballová, Dominika. "Trend Analysis and Detection of Change-Points of Selected Financial and Market Indices." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_30.

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Park, Chiwoo, and Yu Ding. "Change Point Detection." In Data Science for Nano Image Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72822-9_9.

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Sy, Bon K., and Arjun K. Gupta. "Change Point Detection Techniques." In The Kluwer International Series in Engineering and Computer Science. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9001-3_7.

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Chang, Seo-Won, Yong-Ik Byun, and Jaegyoon Hahm. "Variability Detection by Change-Point Analysis." In Lecture Notes in Statistics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_48.

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Isupova, Olga. "Change Point Detection with Gaussian Processes." In Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75508-3_5.

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Horváth, Lajos, Zsuzsanna Horváth, and Marie Hušková. "Ratio tests for change point detection." In Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000220.

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Tatti, Nikolaj. "Fast Likelihood-Based Change Point Detection." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46150-8_39.

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Staude, Gerhard, and Werner Wolf. "Change-Point Detection in Kinetic Signals." In Medical Data Analysis. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_7.

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Brodsky, B. E., and B. S. Darkhovsky. "Disorder Detection of Random Fields." In Nonparametric Methods in Change-Point Problems. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8163-9_5.

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Mayer, Brandon A., and Joseph L. Mundy. "Change Point Geometry for Change Detection in Surveillance Video." In Image Analysis. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_31.

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Conference papers on the topic "Change point and trend detection"

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Klyushin, Dmitriy, and Kateryna Golubeva. "Nonparametric Multiple Comparison Test for Change-Point Detection in Big Data." In 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT). IEEE, 2020. http://dx.doi.org/10.1109/atit50783.2020.9349323.

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Srivastava, Abhishek, P. K. Kapur, and Deepti Mehrotra. "Modelling fault detection with change-point in agile software development environment." In 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). IEEE, 2017. http://dx.doi.org/10.1109/ictus.2017.8286023.

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DePold, Hans, Jason Seigel, Allan Volponi, and Jonthan Hull. "Validation of Diagnostic Data With Statistical Analysis and Embedded Knowledge." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38764.

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The method described in this paper is intended to improve data stability and reduce dispersion of a given parameter signal by replacing any point deemed to be noise (e.g. an outlier) with an estimate of the parameter based on its recent history. This method determines a point is an outlier by utilizing learning and analytical elements that automatically adjust to the dispersion of the input data and automatically model the underlying process. The analytical elements of this method contain two types of embedded knowledge. The first is physics based engineering knowledge based on known interrela

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Domaradzki, Andrzej. "AIS for Trend Change Detection." In 2007 6th International Conference on Computer Information Systems and Industrial Management Applications. IEEE, 2007. http://dx.doi.org/10.1109/cisim.2007.10.

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Chen, Wenhua, and C. C. Jay Kuo. "Change-point detection using wavelets." In Aerospace/Defense Sensing and Controls, edited by Joseph Picone. SPIE, 1996. http://dx.doi.org/10.1117/12.241984.

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Xie, Yao, Meng Wang, and Andrew Thompson. "Sketching for sequential change-point detection." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418160.

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Solomentsev, Olexander, Maksym Zaliskyi, and Tetyana Gerasymenko. "Change-point detection during radar operation." In 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583562.

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Yao Xie and D. Siegmund. "Sequential multi-sensor change-point detection." In 2013 Information Theory and Applications Workshop (ITA 2013). IEEE, 2013. http://dx.doi.org/10.1109/ita.2013.6502987.

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Canzanese, Raymond, Moshe Kam, and Spiros Mancoridis. "Multi-channel Change-Point Malware Detection." In 2013 7th IEEE International Conference on Software Security and Reliability (SERE). IEEE, 2013. http://dx.doi.org/10.1109/sere.2013.20.

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Bouchikhi, Ikram, Andre Ferrari, Cedric Richard, Anthony Bourrier, and Marc Bernot. "Kernel Based Online Change Point Detection." In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902582.

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Reports on the topic "Change point and trend detection"

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Siegmund, David. Change-Point Detection and Adaptive Control of Time-Varying Systems. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada273509.

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Tsunokai, Manabu. Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada627305.

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Mei, Yajun. Robust Rapid Change-Point Detection in Multi-Sensor Data Fusion and Behavior Research. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada557750.

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Rahmani, Mehran, and Manan Naik. Structural Identification and Damage Detection in Bridges using Wave Method and Uniform Shear Beam Models: A Feasibility Study. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1934.

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This report presents a wave method to be used for the structural identification and damage detection of structural components in bridges, e.g., bridge piers. This method has proven to be promising when applied to real structures and large amplitude responses in buildings (e.g., mid-rise and high-rise buildings). This study is the first application of the method to damaged bridge structures. The bridge identification was performed using wave propagation in a simple uniform shear beam model. The method identifies a wave velocity for the structure by fitting an equivalent uniform shear beam model

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McKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, 2021. http://dx.doi.org/10.5204/rep.eprints.211133.

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Emergency Relief (ER) is a Department of Social Services (DSS) funded program, delivered by 197 community organisations (ER Providers) across Australia, to assist people facing a financial crisis with financial/material aid and referrals to other support programs. ER has been playing this important role in Australian communities since 1979. Without ER, more people living in Australia who experience a financial crisis might face further harm such as crippling debt or homelessness. The Emergency Relief National Coordination Group (NCG) was established in April 2020 at the start of the COVID-19 p

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