Academic literature on the topic 'Temporal data analysis'
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Journal articles on the topic "Temporal data analysis":
Purdon, Patrick, Victor Solo, and Emery Brown. "Spatio-temporal longitudinal data analysis." NeuroImage 11, no. 5 (May 2000): S654. http://dx.doi.org/10.1016/s1053-8119(00)91584-2.
Kyriazis, Vasilios, and Constantinos Rigas. "Software for temporal gait data analysis." Computer Methods and Programs in Biomedicine 67, no. 3 (March 2002): 225–29. http://dx.doi.org/10.1016/s0169-2607(01)00152-3.
Haimberger, Leopold. "Checking the temporal homogeneity of radiosonde data in the Alpine region using ERA-40 analysis feedback data." Meteorologische Zeitschrift 13, no. 2 (May 6, 2004): 123–29. http://dx.doi.org/10.1127/0941-2948/2004/0013-0123.
Lesauskiene, E., and K. Dučinskas. "UNIVERSAL KRIGING FOR SPATIO‐TEMPORAL DATA." Mathematical Modelling and Analysis 8, no. 4 (December 31, 2003): 283–90. http://dx.doi.org/10.3846/13926292.2003.9637230.
He, Xing, Qian Ai, Bo Pan, Lei Tang, and Robert Qiu. "Spatial-temporal data analysis of digital twin." Digital Twin 2 (April 19, 2022): 7. http://dx.doi.org/10.12688/digitaltwin.17446.1.
Krzyśko, Mirosław, Waldemar Wołyński, Wojciech Łukaszonek, and Waldemar Ratajczak. "PRINCIPAL COMPONENT ANALYSIS FOR TEMPORAL-SPATIAL DATA." PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU, no. 507 (2018): 115–23. http://dx.doi.org/10.15611/pn.2018.507.11.
Juuso, Esko K. "Intelligent temporal analysis of coronavirus statistical data." Open Engineering 11, no. 1 (January 1, 2021): 1223–32. http://dx.doi.org/10.1515/eng-2021-0118.
Riedl, M., N. Marwan, and J. Kurths. "Multiscale recurrence analysis of spatio-temporal data." Chaos: An Interdisciplinary Journal of Nonlinear Science 25, no. 12 (December 2015): 123111. http://dx.doi.org/10.1063/1.4937164.
Bernardeschi, Cinzia, Andrea Bondavalli, Gyorgy Csertán, Istvan Majzik, and Luca Simoncini. "Temporal analysis of data flow control systems." Automatica 34, no. 2 (February 1998): 169–82. http://dx.doi.org/10.1016/s0005-1098(97)00176-3.
Naya, Futoshi, and Hiroshi Sawada. "From Multidimensional Mixture Data Analysis to Spatio-temporal Multidimensional Collective Data Analysis." NTT Technical Review 14, no. 2 (February 2016): 14–20. http://dx.doi.org/10.53829/ntr201602fa2.
Dissertations / Theses on the topic "Temporal data analysis":
Aßfalg, Johannes. "Advanced Analysis on Temporal Data." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87985.
Goodwin, David Alexander. "Wavelet analysis of temporal data." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/102/.
Hönel, 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.
Kim, Kihwan. "Spatio-temporal data interpolation for dynamic scene analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.
Sutherland, Alasdair. "Analogue VLSI for temporal frequency analysis of visual data." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/11441.
Wu, Elizabeth. "Spatio-Temporal Data Mining and Analysis of Precipitation Extremes." Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28120.
Tao, 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.
Includes 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.
growing 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.
Yang, Kit-ling, and 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.
Books on the topic "Temporal data analysis":
Cressie, Noel A. C. Statistics for spatio-temporal data. Hoboken, N.J: Wiley, 2011.
Hsu, Wynne. Temporal and spatio-temporal data mining. Hershey PA: Idea Group Pub., 2008.
Douzal-Chouakria, Ahlame, José A. Vilar, and Pierre-François Marteau, eds. Advanced Analysis and Learning on Temporal Data. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44412-3.
Sherman, Michael. Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Chichester, West Sussex, U.K: Wiley, 2011.
Ott, Thomas. Time-integrative geographic information systems: Management and analysis of spatio-temporal data. Berlin: Springer, 2001.
Durrleman, Stanley, Tom Fletcher, Guido Gerig, Marc Niethammer, and Xavier Pennec, eds. 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.
Durrleman, Stanley, Tom Fletcher, Guido Gerig, and Marc Niethammer, eds. 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.
Ott, Thomas. Time-Integrative Geographic Information Systems: Management and Analysis of Spatio-Temporal Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.
International 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. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). Bellingham, Wash: SPIE, 2009.
Wang, Jiaqiu. Shi kong xu lie shu ju fen xi he jian mo. 8th ed. Beijing: Ke xue chu ban she, 2012.
Book chapters on the topic "Temporal data analysis":
Bivand, Roger S., Edzer Pebesma, and 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.
Reich, Brian J., and 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.
Scepi, 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.
Sahu, 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.
Guimarães, Gabriela, and 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.
Chen, Wenjie, Haipeng Shen, and 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.
Nawrot, 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.
Kamps, Oliver, and 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.
Sugimoto, Masaki, Takahiro Ueda, Shogo Okada, Yukio Ohsawa, Yoshiharu Maeno, and 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.
Friedrich, R., V. K. Jirsa, H. Haken, and 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.
Conference papers on the topic "Temporal data analysis":
Wang, Huibing, Yaolin Liu, and 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, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838017.
Walsh, James A., Joanne Zucco, Ross T. Smith, and 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.
Wang, Changwei, Deren Li, Yueming Hu, Xiaofang Wu, and Yu Qi. "Research of spatio-temporal analysis of agricultural pest." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838413.
Shu, Hong, Chao Zhao, and Aiping Xu. "Spatio-temporal statistics for exploratory NDVI image analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838576.
Li, Sha, Hong Shu, and Zhengquan Xu. "A spatial-temporal covariance model for rainfall analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838632.
Zhu, Hongmei, and Yu Luo. "Spatial-temporal database model based on geodatabase." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838541.
Xue, Cunjin, Qing Dong, Jiong Xie, and Fenzhen Su. "Process-oriented research on spatio-temporal dynamic semantics." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838294.
Lin, Zhiyong, and 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, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838418.
Yi, Wenbin, and 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, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838283.
dePonte, J., and 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.
Reports on the topic "Temporal data analysis":
Stein, Michael. Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1346562.
Iregui-Bohórquez, Ana María, and 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.
Liming, Kieran. Tests of Equality Between Groups of Spatially Correlated Temporal Data using a Functional Data Analysis Approach. Ames (Iowa): Iowa State University, December 2020. http://dx.doi.org/10.31274/cc-20240624-1391.
DeMarle, David, and Andrew Bauer. In situ visualization with temporal caching. Engineer Research and Development Center (U.S.), January 2022. http://dx.doi.org/10.21079/11681/43042.
Hulsegge, Ina, Henri Woelders, Annemarie Rebel, Mari Smits, and 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.
Khalilzadeh, Fatemeh. Evaluating the Temporal Coverage, Reliability, and Contribution of Incident Detection Sources Using Big Data Analysis. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-651.
Seema, Seema, Andreas Theocharis, and 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.
Nitta, 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, July 2013. http://dx.doi.org/10.21236/ada587634.
Reyes, Julian, Jeb Williamson, and 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.
Kwon, Jaymin, Yushin Ahn, and 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.