Literatura académica sobre el tema "Change point and trend detection"
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Artículos de revistas sobre el tema "Change point and trend detection"
Militino, Ana, Mehdi Moradi y M. Ugarte. "On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data". Remote Sensing 12, n.º 6 (21 de marzo de 2020): 1008. http://dx.doi.org/10.3390/rs12061008.
Texto completoIshak, Elias y Ataur Rahman. "Examination of Changes in Flood Data in Australia". Water 11, n.º 8 (20 de agosto de 2019): 1734. http://dx.doi.org/10.3390/w11081734.
Texto completoAlashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, n.º 3 (21 de febrero de 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.002.
Texto completoAlashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, n.º 3 (21 de febrero de 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.02.
Texto completoWehbe, Youssef y Marouane Temimi. "A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula". Remote Sensing 13, n.º 2 (13 de enero de 2021): 247. http://dx.doi.org/10.3390/rs13020247.
Texto completoVaman, H. J. y K. Suresh Chandra. "OPTIMAL CHANGE-POINT DETECTION IN TREND MODELS WITH INTEGRATED MOVING AVERAGE ERRORS". Sequential Analysis 21, n.º 1-2 (20 de mayo de 2002): 99–107. http://dx.doi.org/10.1081/sqa-120004175.
Texto completoRay, Litan Kumar, Narendra Kumar Goel y Manohar Arora. "Trend analysis and change point detection of temperature over parts of India". Theoretical and Applied Climatology 138, n.º 1-2 (23 de febrero de 2019): 153–67. http://dx.doi.org/10.1007/s00704-019-02819-7.
Texto completoSherwood, Steven C. "Simultaneous Detection of Climate Change and Observing Biases in a Network with Incomplete Sampling". Journal of Climate 20, n.º 15 (1 de agosto de 2007): 4047–62. http://dx.doi.org/10.1175/jcli4215.1.
Texto completoAlhathloul, Saleh H., Abdul A. Khan y Ashok K. Mishra. "Trend analysis and change point detection of annual and seasonal horizontal visibility trends in Saudi Arabia". Theoretical and Applied Climatology 144, n.º 1-2 (24 de enero de 2021): 127–46. http://dx.doi.org/10.1007/s00704-021-03533-z.
Texto completoNguyen, Khanh Ninh, Annarosa Quarello, Olivier Bock y Emilie Lebarbier. "Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties". Atmosphere 12, n.º 9 (26 de agosto de 2021): 1102. http://dx.doi.org/10.3390/atmos12091102.
Texto completoTesis sobre el tema "Change point and trend detection"
Petersson, David y 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.
Texto completoAktiemarknaden 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.
Texto completoSedan 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.
Texto completoGarreau, Damien. "Change-point detection and kernel methods". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE061/document.
Texto completoIn 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 y Heping Zhang. "Multiple Change-Point Detection: A Selective Overview". INST MATHEMATICAL STATISTICS, 2016. http://hdl.handle.net/10150/622820.
Texto completoYang, Ping. "Adaptive trend change detection and pattern recognition in physiological monitoring". Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/8932.
Texto completoMei, 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.
Texto completoGeng, Jun. "Quickest Change-Point Detection with Sampling Right Constraints". Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/440.
Texto completoSchröder, 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/.
Texto completoLibros sobre el tema "Change point and trend detection"
Olympia, Hadjiliadis, ed. Quickest detection. Cambridge: Cambridge University Press, 2009.
Buscar texto completoRahat, Gideon y Ofer Kenig. A Cross-National Comparison of Party Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808008.003.0005.
Texto completoKennett, Douglas J. y 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.
Texto completoRahat, Gideon y Ofer Kenig. From Party Politics to Personalized Politics? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198808008.001.0001.
Texto completoFensholt, Rasmus, Cheikh Mbow, Martin Brandt y Kjeld Rasmussen. Desertification and Re-Greening of the Sahel. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.553.
Texto completoKenyon, Ian R. Quantum 20/20. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198808350.001.0001.
Texto completoCapítulos de libros sobre el tema "Change point and trend detection"
Ballová, Dominika. "Trend Analysis and Detection of Change-Points of Selected Financial and Market Indices". En 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.
Texto completoPark, Chiwoo y Yu Ding. "Change Point Detection". En 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.
Texto completoSy, Bon K. y Arjun K. Gupta. "Change Point Detection Techniques". En 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.
Texto completoChang, Seo-Won, Yong-Ik Byun y Jaegyoon Hahm. "Variability Detection by Change-Point Analysis". En 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.
Texto completoIsupova, Olga. "Change Point Detection with Gaussian Processes". En 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.
Texto completoHorváth, Lajos, Zsuzsanna Horváth y Marie Hušková. "Ratio tests for change point detection". En 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.
Texto completoTatti, Nikolaj. "Fast Likelihood-Based Change Point Detection". En 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.
Texto completoStaude, Gerhard y Werner Wolf. "Change-Point Detection in Kinetic Signals". En Medical Data Analysis, 43–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39949-6_7.
Texto completoBrodsky, B. E. y B. S. Darkhovsky. "Disorder Detection of Random Fields". En Nonparametric Methods in Change-Point Problems, 151–68. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-015-8163-9_5.
Texto completoMayer, Brandon A. y Joseph L. Mundy. "Change Point Geometry for Change Detection in Surveillance Video". En Image Analysis, 377–87. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19665-7_31.
Texto completoActas de conferencias sobre el tema "Change point and trend detection"
Klyushin, Dmitriy y Kateryna Golubeva. "Nonparametric Multiple Comparison Test for Change-Point Detection in Big Data". En 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT). IEEE, 2020. http://dx.doi.org/10.1109/atit50783.2020.9349323.
Texto completoSrivastava, Abhishek, P. K. Kapur y Deepti Mehrotra. "Modelling fault detection with change-point in agile software development environment". En 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.
Texto completoDePold, Hans, Jason Seigel, Allan Volponi y Jonthan Hull. "Validation of Diagnostic Data With Statistical Analysis and Embedded Knowledge". En ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38764.
Texto completoDomaradzki, Andrzej. "AIS for Trend Change Detection". En 2007 6th International Conference on Computer Information Systems and Industrial Management Applications. IEEE, 2007. http://dx.doi.org/10.1109/cisim.2007.10.
Texto completoChen, Wenhua y C. C. Jay Kuo. "Change-point detection using wavelets". En Aerospace/Defense Sensing and Controls, editado por Joseph Picone. SPIE, 1996. http://dx.doi.org/10.1117/12.241984.
Texto completoXie, Yao, Meng Wang y Andrew Thompson. "Sketching for sequential change-point detection". En 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418160.
Texto completoSolomentsev, Olexander, Maksym Zaliskyi y Tetyana Gerasymenko. "Change-point detection during radar operation". En 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). IEEE, 2016. http://dx.doi.org/10.1109/dsmp.2016.7583562.
Texto completoYao Xie y D. Siegmund. "Sequential multi-sensor change-point detection". En 2013 Information Theory and Applications Workshop (ITA 2013). IEEE, 2013. http://dx.doi.org/10.1109/ita.2013.6502987.
Texto completoCanzanese, Raymond, Moshe Kam y Spiros Mancoridis. "Multi-channel Change-Point Malware Detection". En 2013 7th IEEE International Conference on Software Security and Reliability (SERE). IEEE, 2013. http://dx.doi.org/10.1109/sere.2013.20.
Texto completoBouchikhi, Ikram, Andre Ferrari, Cedric Richard, Anthony Bourrier y Marc Bernot. "Kernel Based Online Change Point Detection". En 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902582.
Texto completoInformes sobre el tema "Change point and trend detection"
Siegmund, David. Change-Point Detection and Adaptive Control of Time-Varying Systems. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 1993. http://dx.doi.org/10.21236/ada273509.
Texto completoTsunokai, Manabu. Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance. Fort Belvoir, VA: Defense Technical Information Center, agosto de 2015. http://dx.doi.org/10.21236/ada627305.
Texto completoMei, Yajun. Robust Rapid Change-Point Detection in Multi-Sensor Data Fusion and Behavior Research. Fort Belvoir, VA: Defense Technical Information Center, febrero de 2011. http://dx.doi.org/10.21236/ada557750.
Texto completoRahmani, Mehran y Manan Naik. Structural Identification and Damage Detection in Bridges using Wave Method and Uniform Shear Beam Models: A Feasibility Study. Mineta Transportation Institute, febrero de 2021. http://dx.doi.org/10.31979/mti.2021.1934.
Texto completoMcKenna, Patrick y Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, junio de 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
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