Academic literature on the topic 'Change point and trend detection'
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Journal articles on the topic "Change point and trend detection"
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 (March 21, 2020): 1008. http://dx.doi.org/10.3390/rs12061008.
Full textIshak, Elias, and Ataur Rahman. "Examination of Changes in Flood Data in Australia." Water 11, no. 8 (August 20, 2019): 1734. http://dx.doi.org/10.3390/w11081734.
Full textAlashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (February 21, 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.002.
Full textAlashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (February 21, 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.02.
Full textWehbe, Youssef, and Marouane Temimi. "A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula." Remote Sensing 13, no. 2 (January 13, 2021): 247. http://dx.doi.org/10.3390/rs13020247.
Full textVaman, H. J., and K. Suresh Chandra. "OPTIMAL CHANGE-POINT DETECTION IN TREND MODELS WITH INTEGRATED MOVING AVERAGE ERRORS." Sequential Analysis 21, no. 1-2 (May 20, 2002): 99–107. http://dx.doi.org/10.1081/sqa-120004175.
Full textRay, 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 (February 23, 2019): 153–67. http://dx.doi.org/10.1007/s00704-019-02819-7.
Full textSherwood, Steven C. "Simultaneous Detection of Climate Change and Observing Biases in a Network with Incomplete Sampling." Journal of Climate 20, no. 15 (August 1, 2007): 4047–62. http://dx.doi.org/10.1175/jcli4215.1.
Full textAlhathloul, 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 (January 24, 2021): 127–46. http://dx.doi.org/10.1007/s00704-021-03533-z.
Full textNguyen, 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 (August 26, 2021): 1102. http://dx.doi.org/10.3390/atmos12091102.
Full textDissertations / Theses on the topic "Change point and trend detection"
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.
Full textAktiemarknaden kan vara en hård och oförlåtande plats att investera sina pengar i som novis. För att ha någon chans att gå med vinst krävs oräkneligt många timmars efterforskning av företag och dess möjligheter. Vidare bör man sprida sina investeringar över flertalet oberoende branscher och på så sätt minska risken för stora förluster. Med många aktörer och en stor mängd parametrar som måste falla samman kan detta verka näst intill omöjligt att klara av som privatperson. Med modern teknologi finns nu stor potential till att kunna hantera dessa analyser autonomt med maskininlärning. Om man ser på problemet från denna infallsvinkel inser man snart att analysförmågan enbart begränsas av vilken datorkraft man besitter. Denna studie utforskar möjligheterna kring maskininlärning inom teknisk analys genom att kombinera effektiva algoritmer på ett nytänkande sätt. Genom att utnyttja kraften bakom kernel-metoder kan mönster i finansiella data analyseras effektivt. En ny kombination, av ickelinjär regression och algoritmer som är kapabla till att hitta brytpunkter i mönster, föreslås. Slutprodukten från denna studie är ett analysverktyg som minimerar influensen från plötsliga händelser och istället ger större vikt till de underliggande mönstren i finansiella data. Det introduceras också ett ytterligare verktyg som kan användas för att estimera framtida prisrörelser.
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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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.
Full textSedan mitten av förra århundradet har den antropogena påverkan på jordens system ökat kraftigt. Idag är det svårt att hitta ett vattendrag som inte är påverkat av mänsklig aktivitet. Att förstå orsakerna bakom förändringarna är en viktig kunskap för framtida vattenplanering och av denna anledning undersöktes och kvantiferades den antropogen och klimatpåverkan på flödesförändringar i svenska vattendrag. I arbetets första steg användes de Mann-Kendalls och Pettitts test för att lokalisera och verifiera förändringar i årligt vattenflöde. Alla test var icke parametriska och utfördes som ett glidande fönster. I nästa steg undersöktes orsakerna till förändringar med hjälp av HBV, en klimatdriven avrinningsmodell. Ett större antal avrinningsområden undersöktes för att upptäcka regionala mönster och skillnader. Perioder med omväxlande positiva och negativa trender upptäcktes med mindre fönsterstorlekar, medan större fönster hittade positiva trender i mer än hälften av områdena och knappt några negativa trender hittades. De detekterade förändringarna var på grund av periodicitet i årligt vattenflöde till stor grad beroende på det undersöka tidsintervallet. Generellt var den antropogena påverkan större påverkan från nederbörd och temperatur, med ett medianvärde där 7 % av den totala förändringen kunde förklaras med antropogen påverkan. Inga regionala skillnader i antropogen påverkan kunde identifieras vilket indikerar att den varierar mer mellan individuella områden än följer ett regionalt mönster.
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.
Full textGarreau, Damien. "Change-point detection and kernel methods." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE061/document.
Full textIn this thesis, we focus on a method for detecting abrupt changes in a sequence of independent observations belonging to an arbitrary set on which a positive semidefinite kernel is defined. That method, kernel changepoint detection, is a kernelized version of a penalized least-squares procedure. Our main contribution is to show that, for any kernel satisfying some reasonably mild hypotheses, this procedure outputs a segmentation close to the true segmentation with high probability. This result is obtained under a bounded assumption on the kernel for a linear penalty and for another penalty function, coming from model selection.The proofs rely on a concentration result for bounded random variables in Hilbert spaces and we prove a less powerful result under relaxed hypotheses—a finite variance assumption. In the asymptotic setting, we show that we recover the minimax rate for the change-point locations without additional hypothesis on the segment sizes. We provide empirical evidence supporting these claims. Another contribution of this thesis is the detailed presentation of the different notions of distances between segmentations. Additionally, we prove a result showing these different notions coincide for sufficiently close segmentations.From a practical point of view, we demonstrate how the so-called dimension jump heuristic can be a reasonable choice of penalty constant when using kernel changepoint detection with a linear penalty. We also show how a key quantity depending on the kernelthat appears in our theoretical results influences the performance of kernel change-point detection in the case of a single change-point. When the kernel is translationinvariant and parametric assumptions are made, it is possible to compute this quantity in closed-form. Thanks to these computations, some of them novel, we are able to study precisely the behavior of the maximal penalty constant. Finally, we study the median heuristic, a popular tool to set the bandwidth of radial basis function kernels. Fora large sample size, we show that it behaves approximately as the median of a distribution that we describe completely in the setting of kernel two-sample test and kernel change-point detection. More precisely, we show that the median heuristic is asymptotically normal around this value
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.
Full textYang, Ping. "Adaptive trend change detection and pattern recognition in physiological monitoring." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/8932.
Full textMei, 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.
Full textGeng, Jun. "Quickest Change-Point Detection with Sampling Right Constraints." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/440.
Full textSchröder, 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/.
Full textBooks on the topic "Change point and trend detection"
Olympia, Hadjiliadis, ed. Quickest detection. Cambridge: Cambridge University Press, 2009.
Find full textRahat, 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.
Full textKennett, 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.
Full textRahat, Gideon, and Ofer Kenig. From Party Politics to Personalized Politics? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808008.001.0001.
Full textFensholt, 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.
Full textKenyon, Ian R. Quantum 20/20. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198808350.001.0001.
Full textBook chapters on the topic "Change point and trend detection"
Ballová, Dominika. "Trend Analysis and Detection of Change-Points of Selected Financial and Market Indices." In Advances in Intelligent Systems and Computing, 372–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18058-4_30.
Full textPark, Chiwoo, and Yu Ding. "Change Point Detection." In Data Science for Nano Image Analysis, 241–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72822-9_9.
Full textSy, Bon K., and Arjun K. Gupta. "Change Point Detection Techniques." In The Kluwer International Series in Engineering and Computer Science, 93–98. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9001-3_7.
Full textChang, Seo-Won, Yong-Ik Byun, and Jaegyoon Hahm. "Variability Detection by Change-Point Analysis." In Lecture Notes in Statistics, 491–93. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3520-4_48.
Full textIsupova, Olga. "Change Point Detection with Gaussian Processes." In Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video, 83–104. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75508-3_5.
Full textHorváth, 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, 293–304. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000220.
Full textTatti, Nikolaj. "Fast Likelihood-Based Change Point Detection." In Machine Learning and Knowledge Discovery in Databases, 662–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46150-8_39.
Full textStaude, Gerhard, and Werner Wolf. "Change-Point Detection in Kinetic Signals." In Medical Data Analysis, 43–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_7.
Full textBrodsky, B. E., and B. S. Darkhovsky. "Disorder Detection of Random Fields." In Nonparametric Methods in Change-Point Problems, 151–68. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8163-9_5.
Full textMayer, Brandon A., and Joseph L. Mundy. "Change Point Geometry for Change Detection in Surveillance Video." In Image Analysis, 377–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_31.
Full textConference papers on the topic "Change point and trend detection"
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.
Full textSrivastava, 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.
Full textDePold, 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.
Full textDomaradzki, 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.
Full textChen, 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.
Full textXie, 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.
Full textSolomentsev, 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.
Full textYao 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.
Full textCanzanese, 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.
Full textBouchikhi, 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.
Full textReports on the topic "Change point and trend detection"
Siegmund, David. Change-Point Detection and Adaptive Control of Time-Varying Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada273509.
Full textTsunokai, Manabu. Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada627305.
Full textMei, Yajun. Robust Rapid Change-Point Detection in Multi-Sensor Data Fusion and Behavior Research. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada557750.
Full textRahmani, 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, February 2021. http://dx.doi.org/10.31979/mti.2021.1934.
Full textMcKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, June 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
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