Auswahl der wissenschaftlichen Literatur zum Thema „Temporal data analysis“
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Zeitschriftenartikel zum Thema "Temporal data analysis"
Purdon, Patrick, Victor Solo und Emery Brown. „Spatio-temporal longitudinal data analysis“. NeuroImage 11, Nr. 5 (Mai 2000): S654. http://dx.doi.org/10.1016/s1053-8119(00)91584-2.
Der volle Inhalt der QuelleKyriazis, Vasilios, und Constantinos Rigas. „Software for temporal gait data analysis“. Computer Methods and Programs in Biomedicine 67, Nr. 3 (März 2002): 225–29. http://dx.doi.org/10.1016/s0169-2607(01)00152-3.
Der volle Inhalt der QuelleHaimberger, Leopold. „Checking the temporal homogeneity of radiosonde data in the Alpine region using ERA-40 analysis feedback data“. Meteorologische Zeitschrift 13, Nr. 2 (06.05.2004): 123–29. http://dx.doi.org/10.1127/0941-2948/2004/0013-0123.
Der volle Inhalt der QuelleLesauskiene, E., und K. Dučinskas. „UNIVERSAL KRIGING FOR SPATIO‐TEMPORAL DATA“. Mathematical Modelling and Analysis 8, Nr. 4 (31.12.2003): 283–90. http://dx.doi.org/10.3846/13926292.2003.9637230.
Der volle Inhalt der QuelleHe, Xing, Qian Ai, Bo Pan, Lei Tang und Robert Qiu. „Spatial-temporal data analysis of digital twin“. Digital Twin 2 (19.04.2022): 7. http://dx.doi.org/10.12688/digitaltwin.17446.1.
Der volle Inhalt der QuelleKrzyśko, Mirosław, Waldemar Wołyński, Wojciech Łukaszonek und Waldemar Ratajczak. „PRINCIPAL COMPONENT ANALYSIS FOR TEMPORAL-SPATIAL DATA“. PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU, Nr. 507 (2018): 115–23. http://dx.doi.org/10.15611/pn.2018.507.11.
Der volle Inhalt der QuelleJuuso, Esko K. „Intelligent temporal analysis of coronavirus statistical data“. Open Engineering 11, Nr. 1 (01.01.2021): 1223–32. http://dx.doi.org/10.1515/eng-2021-0118.
Der volle Inhalt der QuelleRiedl, M., N. Marwan und J. Kurths. „Multiscale recurrence analysis of spatio-temporal data“. Chaos: An Interdisciplinary Journal of Nonlinear Science 25, Nr. 12 (Dezember 2015): 123111. http://dx.doi.org/10.1063/1.4937164.
Der volle Inhalt der QuelleBernardeschi, Cinzia, Andrea Bondavalli, Gyorgy Csertán, Istvan Majzik und Luca Simoncini. „Temporal analysis of data flow control systems“. Automatica 34, Nr. 2 (Februar 1998): 169–82. http://dx.doi.org/10.1016/s0005-1098(97)00176-3.
Der volle Inhalt der QuelleNaya, Futoshi, und Hiroshi Sawada. „From Multidimensional Mixture Data Analysis to Spatio-temporal Multidimensional Collective Data Analysis“. NTT Technical Review 14, Nr. 2 (Februar 2016): 14–20. http://dx.doi.org/10.53829/ntr201602fa2.
Der volle Inhalt der QuelleDissertationen zum Thema "Temporal data analysis"
Aßfalg, Johannes. „Advanced Analysis on Temporal Data“. Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87985.
Der volle Inhalt der QuelleGoodwin, David Alexander. „Wavelet analysis of temporal data“. Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/102/.
Der volle Inhalt der QuelleHönel, Sebastian. „Temporal data analysis facilitating recognition of enhanced patterns“. Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-51864.
Der volle Inhalt der QuelleKim, Kihwan. „Spatio-temporal data interpolation for dynamic scene analysis“. Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.
Der volle Inhalt der QuelleSutherland, Alasdair. „Analogue VLSI for temporal frequency analysis of visual data“. Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/11441.
Der volle Inhalt der QuelleWu, Elizabeth. „Spatio-Temporal Data Mining and Analysis of Precipitation Extremes“. Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28120.
Der volle Inhalt der QuelleTao, Yufei. „Indexing and query processing of spatio-temporal data /“. View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.
Der volle Inhalt der QuelleIncludes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
Chen, Feng. „Efficient Algorithms for Mining Large Spatio-Temporal Data“. Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19220.
Der volle Inhalt der Quellegrowing interests. Recent advances on remote sensing technology mean
that massive amounts of spatio-temporal data are being collected,
and its volume keeps increasing at an ever faster pace. It becomes
critical to design efficient algorithms for identifying novel and
meaningful patterns from massive spatio-temporal datasets. Different
from the other data sources, this data exhibits significant
space-time statistical dependence, and the assumption of i.i.d. is
no longer valid. The exact modeling of space-time dependence will
render the exponential growth of model complexity as the data size
increases. This research focuses on the construction of efficient
and effective approaches using approximate inference techniques for
three main mining tasks, including spatial outlier detection, robust
spatio-temporal prediction, and novel applications to real world
problems.
Spatial novelty patterns, or spatial outliers, are those data points
whose characteristics are markedly different from their spatial
neighbors. There are two major branches of spatial outlier detection
methodologies, which can be either global Kriging based or local
Laplacian smoothing based. The former approach requires the exact
modeling of spatial dependence, which is time extensive; and the
latter approach requires the i.i.d. assumption of the smoothed
observations, which is not statistically solid. These two approaches
are constrained to numerical data, but in real world applications we
are often faced with a variety of non-numerical data types, such as
count, binary, nominal, and ordinal. To summarize, the main research
challenges are: 1) how much spatial dependence can be eliminated via
Laplace smoothing; 2) how to effectively and efficiently detect
outliers for large numerical spatial datasets; 3) how to generalize
numerical detection methods and develop a unified outlier detection
framework suitable for large non-numerical datasets; 4) how to
achieve accurate spatial prediction even when the training data has
been contaminated by outliers; 5) how to deal with spatio-temporal
data for the preceding problems.
To address the first and second challenges, we mathematically
validated the effectiveness of Laplacian smoothing on the
elimination of spatial autocorrelations. This work provides
fundamental support for existing Laplacian smoothing based methods.
We also discovered a nontrivial side-effect of Laplacian smoothing,
which ingests additional spatial variations to the data due to
convolution effects. To capture this extra variability, we proposed
a generalized local statistical model, and designed two fast forward
and backward outlier detection methods that achieve a better balance
between computational efficiency and accuracy than most existing
methods, and are well suited to large numerical spatial datasets.
We addressed the third challenge by mapping non-numerical variables
to latent numerical variables via a link function, such as logit
function used in logistic regression, and then utilizing
error-buffer artificial variables, which follow a Student-t
distribution, to capture the large valuations caused by outliers. We
proposed a unified statistical framework, which integrates the
advantages of spatial generalized linear mixed model, robust spatial
linear model, reduced-rank dimension reduction, and Bayesian
hierarchical model. A linear-time approximate inference algorithm
was designed to infer the posterior distribution of the error-buffer
artificial variables conditioned on observations. We demonstrated
that traditional numerical outlier detection methods can be directly
applied to the estimated artificial variables for outliers
detection. To the best of our knowledge, this is the first
linear-time outlier detection algorithm that supports a variety of
spatial attribute types, such as binary, count, ordinal, and
nominal.
To address the fourth and fifth challenges, we proposed a robust
version of the Spatio-Temporal Random Effects (STRE) model, namely
the Robust STRE (R-STRE) model. The regular STRE model is a recently
proposed statistical model for large spatio-temporal data that has a
linear order time complexity, but is not best suited for
non-Gaussian and contaminated datasets. This deficiency can be
systemically addressed by increasing the robustness of the model
using heavy-tailed distributions, such as the Huber, Laplace, or
Student-t distribution to model the measurement error, instead of
the traditional Gaussian. However, the resulting R-STRE model
becomes analytical intractable, and direct application of
approximate inferences techniques still has a cubic order time
complexity. To address the computational challenge, we reformulated
the prediction problem as a maximum a posterior (MAP) problem with a
non-smooth objection function, transformed it to a equivalent
quadratic programming problem, and developed an efficient
interior-point numerical algorithm with a near linear order
complexity. This work presents the first near linear time robust
prediction approach for large spatio-temporal datasets in both
offline and online cases.
Ph. D.
Wang, Zilong. „Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models“. UKnowledge, 2012. http://uknowledge.uky.edu/statistics_etds/3.
Der volle Inhalt der QuelleYang, Kit-ling, und 楊潔玲. „Statistical analysis of temporal and spatial variations in suicide data“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841811.
Der volle Inhalt der QuelleBücher zum Thema "Temporal data analysis"
Cressie, Noel A. C. Statistics for spatio-temporal data. Hoboken, N.J: Wiley, 2011.
Den vollen Inhalt der Quelle findenHsu, Wynne. Temporal and spatio-temporal data mining. Hershey PA: Idea Group Pub., 2008.
Den vollen Inhalt der Quelle findenDouzal-Chouakria, Ahlame, José A. Vilar und Pierre-François Marteau, Hrsg. Advanced Analysis and Learning on Temporal Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44412-3.
Der volle Inhalt der QuelleSherman, Michael. Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Chichester, West Sussex, U.K: Wiley, 2011.
Den vollen Inhalt der Quelle findenOtt, Thomas. Time-integrative geographic information systems: Management and analysis of spatio-temporal data. Berlin: Springer, 2001.
Den vollen Inhalt der Quelle findenDurrleman, Stanley, Tom Fletcher, Guido Gerig, Marc Niethammer und Xavier Pennec, Hrsg. Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14905-9.
Der volle Inhalt der QuelleDurrleman, Stanley, Tom Fletcher, Guido Gerig und Marc Niethammer, Hrsg. Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33555-6.
Der volle Inhalt der QuelleOtt, Thomas. Time-Integrative Geographic Information Systems: Management and Analysis of Spatio-Temporal Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.
Den vollen Inhalt der Quelle findenInternational Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Herausgegeben von Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu und SPIE (Society). Bellingham, Wash: SPIE, 2009.
Den vollen Inhalt der Quelle findenWang, Jiaqiu. Shi kong xu lie shu ju fen xi he jian mo. 8. Aufl. Beijing: Ke xue chu ban she, 2012.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Temporal data analysis"
Bivand, Roger S., Edzer Pebesma und Virgilio Gómez-Rubio. „Spatio-Temporal Data“. In Applied Spatial Data Analysis with R, 151–66. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7618-4_6.
Der volle Inhalt der QuelleReich, Brian J., und Montserrat Fuentes. „Accounting for Design in the Analysis of Spatial Data“. In Spatio-Temporal Design, 131–41. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118441862.ch6.
Der volle Inhalt der QuelleScepi, Germana. „Clustering Algorithms for Large Temporal Data Sets“. In Data Analysis and Classification, 369–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03739-9_42.
Der volle Inhalt der QuelleSahu, Sujit K. „Exploratory data analysis methods“. In Bayesian Modeling of Spatio-Temporal Data with R, 49–68. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429318443-3.
Der volle Inhalt der QuelleGuimarães, Gabriela, und Alfred Ultsch. „A Method for Temporal Knowledge Conversion“. In Advances in Intelligent Data Analysis, 369–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48412-4_31.
Der volle Inhalt der QuelleChen, Wenjie, Haipeng Shen und Young K. Truong. „Spatio-Temporal Modeling for fMRI Data“. In Time Series Analysis and Forecasting, 293–311. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28725-6_22.
Der volle Inhalt der QuelleNawrot, Ilona. „Gender Differences in Temporal Data Analysis“. In Design, User Experience, and Usability: Users and Interactions, 232–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20898-5_23.
Der volle Inhalt der QuelleKamps, Oliver, und Joachim Peinke. „Analysis of Noisy Spatio-Temporal Data“. In Understanding Complex Systems, 319–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27635-9_22.
Der volle Inhalt der QuelleSugimoto, Masaki, Takahiro Ueda, Shogo Okada, Yukio Ohsawa, Yoshiharu Maeno und Katsumi Nitta. „Discussion Analysis Using Temporal Data Crystallization“. In New Frontiers in Artificial Intelligence, 205–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39931-2_15.
Der volle Inhalt der QuelleFriedrich, R., V. K. Jirsa, H. Haken und C. Uhl. „Analyzing Spatio-Temporal Patterns of Complex Systems“. In Nonlinear Analysis of Physiological Data, 101–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71949-3_7.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Temporal data analysis"
Wang, Huibing, Yaolin Liu und Xinming Tang. „Spatio-temporal data dynamic visualization based on temporal tree structure“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838017.
Der volle Inhalt der QuelleWalsh, James A., Joanne Zucco, Ross T. Smith und Bruce H. Thomas. „Temporal-Geospatial Cooperative Visual Analysis“. In 2016 Big Data Visual Analytics (BDVA). IEEE, 2016. http://dx.doi.org/10.1109/bdva.2016.7787050.
Der volle Inhalt der QuelleWang, Changwei, Deren Li, Yueming Hu, Xiaofang Wu und Yu Qi. „Research of spatio-temporal analysis of agricultural pest“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838413.
Der volle Inhalt der QuelleShu, Hong, Chao Zhao und Aiping Xu. „Spatio-temporal statistics for exploratory NDVI image analysis“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838576.
Der volle Inhalt der QuelleLi, Sha, Hong Shu und Zhengquan Xu. „A spatial-temporal covariance model for rainfall analysis“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838632.
Der volle Inhalt der QuelleZhu, Hongmei, und Yu Luo. „Spatial-temporal database model based on geodatabase“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838541.
Der volle Inhalt der QuelleXue, Cunjin, Qing Dong, Jiong Xie und Fenzhen Su. „Process-oriented research on spatio-temporal dynamic semantics“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838294.
Der volle Inhalt der QuelleLin, Zhiyong, und Shuang Liang. „The application of spatial analysis based on rough set theory and hierarchical analysis“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838418.
Der volle Inhalt der QuelleYi, Wenbin, und Hong Tang. „Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic analysis“. In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, herausgegeben von Yaolin Liu und Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838283.
Der volle Inhalt der QuelledePonte, J., und M. A. Conroy. „Temporal data screening in PROS“. In The soft x-ray cosmos: ROSAT science symposium and data analysis workshop. AIP, 1994. http://dx.doi.org/10.1063/1.46745.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Temporal data analysis"
Stein, Michael. Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes. Office of Scientific and Technical Information (OSTI), März 2017. http://dx.doi.org/10.2172/1346562.
Der volle Inhalt der QuelleIregui-Bohórquez, Ana María, und Jesús Otero. A Spatio-temporal Analysis of Agricultural Prices: An Application to Colombian Data. Bogotá, Colombia: Banco de la República, September 2012. http://dx.doi.org/10.32468/be.734.
Der volle Inhalt der QuelleLiming, Kieran. Tests of Equality Between Groups of Spatially Correlated Temporal Data using a Functional Data Analysis Approach. Ames (Iowa): Iowa State University, Dezember 2020. http://dx.doi.org/10.31274/cc-20240624-1391.
Der volle Inhalt der QuelleDeMarle, David, und Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), Januar 2022. http://dx.doi.org/10.21079/11681/43042.
Der volle Inhalt der QuelleHulsegge, Ina, Henri Woelders, Annemarie Rebel, Mari Smits und Dirkjan Schokker. Meta-analysis of temporal intestinal gene expression data to generate reference profiles: VDI-10. Wageningen: Wageningen Livestock Research, 2017. http://dx.doi.org/10.18174/426338.
Der volle Inhalt der QuelleKhalilzadeh, Fatemeh. Evaluating the Temporal Coverage, Reliability, and Contribution of Incident Detection Sources Using Big Data Analysis. Ames (Iowa): Iowa State University, Januar 2020. http://dx.doi.org/10.31274/cc-20240624-651.
Der volle Inhalt der QuelleSeema, Seema, Andreas Theocharis und Andreas Kassler. Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstad University, 2024. http://dx.doi.org/10.59217/yjll7238.
Der volle Inhalt der QuelleNitta, Katsumi. Development of Meta Level Communication Analysis using Temporal Data Crystallization and Its Application to Multi Modal Human Communication. Fort Belvoir, VA: Defense Technical Information Center, Juli 2013. http://dx.doi.org/10.21236/ada587634.
Der volle Inhalt der QuelleReyes, Julian, Jeb Williamson und Emile Elias. Spatio-temporal analysis of Federal crop insurance cause of loss data: A roadmap for research and outreach effort. U.S. Department of Agriculture, April 2018. http://dx.doi.org/10.32747/2018.7202608.ch.
Der volle Inhalt der QuelleKwon, Jaymin, Yushin Ahn und Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation Related Air Quality (STARTRAQ) and Neighborhood Characterization. Mineta Transportation Institute, August 2021. http://dx.doi.org/10.31979/mti.2021.2010.
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