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Готові списки джерел за темами / Temporal data analysis

Добірка наукової літератури з теми "Temporal data analysis"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Статті в журналах з теми "Temporal data analysis":

1

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.

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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.

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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.

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4

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.

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In this article we have used wide applicable classes of spatio‐temporal nonseparable and separable covariance models. One of the objectives of this paper is to furnish a possibility how to avoid the usage of complicated covariance functions. Assuming regression model for mean function the analytical expressions for the optimal linear prediction (universal kriging) and mean squared prediction error (MSPE) was obtained. Parameterized spatio‐temporal covariance functions were fitted for the real data. Prediction values and MSPE were presented. For visualization of results on graphics are used free available software Gstat.

5

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.

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Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology in DT. The goal of this research is to summarize the best practices that make the spatial-temporal data analytically tractable in a systematic and quantifiable manner. Some methods are found to handle those data via jointly spatial-temporal analysis in a high-dimensional space effectively. We provide a concise yet comprehensive tutorial on spatial-temporal analysis considering data, theories, algorithms, indicators, and applications. The advantages of our spatial-temporal analysis are discussed, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. Finally, we take the condition-based maintenance of a real digital substation in China as an example to verify our proposed spatial-temporal analysis mode. Results: Our proposed spatial-temporal data analysis mode successfully turned raw chromatographic data, which are valueless in low-dimensional space, into an informative high-dimensional indicator. The designed high-dimensional indicator could capture the ’insulation’ correlation among the sampling data over a long time span. Hence it is robust against external noise, and may support decision-making. This analysis is also adaptive to other daily spatial-temporal data in the same form. Conclusions: This exploration and summary of spatial-temporal data analysis may benefit the fields of both engineering and data science.

6

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.

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7

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.

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Abstract The coronavirus COVID-19 is affecting around the world with strong differences between countries and regions. Extensive datasets are available for visual inspection and downloading. The material has limitations for phenomenological modeling but data-based methodologies can be used. This research focuses on the intelligent temporal analysis of datasets in developing compact solutions for early detection of levels, trends, episodes, and severity of situations. The methodology has been tested in the analysis of daily new confirmed COVID-19 cases and deaths in six countries. The datasets are studied per million people to get comparable indicators. Nonlinear scaling brings the data of different countries to the same scale, and the temporal analysis is based on the scaled values. The same approach can be used for any country or a group of people, e.g., hospital patients, patients in intensive care, or people in different age categories. During the pandemic, the scaling functions expanded for the confirmed cases but remained practically unchanged for the confirmed deaths, which is consistent with increasing testing.

8

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.

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9

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.

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10

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.

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Дисертації з теми "Temporal data analysis":

1

Aßfalg, Johannes. "Advanced Analysis on Temporal Data." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87985.

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2

Goodwin, David Alexander. "Wavelet analysis of temporal data." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/102/.

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This thesis considers the application of wavelets to problems involving multiple series of temporal data. Wavelets have proven to be highly effective at extracting frequency information from data. Their multi-scale nature enables the efficient description of both transient and long-term signals. Furthermore, only a small number of wavelet coefficients are needed to describe complicated signals and the wavelet transform is computationally efficient. In problems where frequency properties are known to be important, it is proposed that a modelling approach which attempts to explain the response in terms of a multi-scale wavelet representation of the explanatory series will be an improvement on standard regression techniques. The problem with classical regression is that differing frequency characteristics are not exploited and make the estimates of the model parameters less stable. The proposed modelling method is presented with application to examples from seismology and tomography. In the first part of the thesis, we investigate the use of the non-decimated wavelet transform in the modelling of data produced from a simulated seismology study. The fact that elastic waves travel with different velocities in different rock types is exploited and wavelet models are proposed to avoid the complication of predictions being unstable to small changes in the input data, that is an inverse problem. The second part of the thesis uses the non-decimated wavelet transform to model electrical tomographic data, with the aim of process control. In industrial applications of electrical tomography, multiple voltages are recorded between electrodes attached to the boundary of, for example, a pipe. The usual first step of the analysis is then to reconstruct the conductivity distribution within the pipe. The most commonly used approaches again lead to inverse problems, and wavelet models are again used here to overcome this difficulty. We conclude by developing the non-decimated multi-wavelet transform for use in the modelling processes and investigate the improvements over scalar wavelets.

3

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.

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Assessing the source code quality of software objectively requires a well-defined model. Due to the distinct nature of each and every project, the definition of such a model is specific to the underlying type of paradigms used. A definer can pick metrics from standard norms to define measurements for qualitative assessment. Software projects develop over time and a wide variety of re-factorings is applied tothe code which makes the process temporal. In this thesis the temporal model was enhanced using methods known from financial markets and further evaluated using artificial neural networks with the goal of improving the prediction precision by learning from more detailed patterns. Subject to research was also if the combination of technical analysis and machine learning is viable and how to blend them. An in-depth selection of applicable instruments and algorithms and extensive experiments were run to approximate answers. It was found that enhanced patterns are of value for further processing by neural networks. Technical analysis however was not able to improve the results, although it is assumed that it can for an appropriately sizedproblem set.

4

Kim, Kihwan. "Spatio-temporal data interpolation for dynamic scene analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/47729.

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Analysis and visualization of dynamic scenes is often constrained by the amount of spatio-temporal information available from the environment. In most scenarios, we have to account for incomplete information and sparse motion data, requiring us to employ interpolation and approximation methods to fill for the missing information. Scattered data interpolation and approximation techniques have been widely used for solving the problem of completing surfaces and images with incomplete input data. We introduce approaches for such data interpolation and approximation from limited sensors, into the domain of analyzing and visualizing dynamic scenes. Data from dynamic scenes is subject to constraints due to the spatial layout of the scene and/or the configurations of video cameras in use. Such constraints include: (1) sparsely available cameras observing the scene, (2) limited field of view provided by the cameras in use, (3) incomplete motion at a specific moment, and (4) varying frame rates due to different exposures and resolutions. In this thesis, we establish these forms of incompleteness in the scene, as spatio-temporal uncertainties, and propose solutions for resolving the uncertainties by applying scattered data approximation into a spatio-temporal domain. The main contributions of this research are as follows: First, we provide an efficient framework to visualize large-scale dynamic scenes from distributed static videos. Second, we adopt Radial Basis Function (RBF) interpolation to the spatio-temporal domain to generate global motion tendency. The tendency, represented by a dense flow field, is used to optimally pan and tilt a video camera. Third, we propose a method to represent motion trajectories using stochastic vector fields. Gaussian Process Regression (GPR) is used to generate a dense vector field and the certainty of each vector in the field. The generated stochastic fields are used for recognizing motion patterns under varying frame-rate and incompleteness of the input videos. Fourth, we also show that the stochastic representation of vector field can also be used for modeling global tendency to detect the region of interests in dynamic scenes with camera motion. We evaluate and demonstrate our approaches in several applications for visualizing virtual cities, automating sports broadcasting, and recognizing traffic patterns in surveillance videos.

5

Sutherland, Alasdair. "Analogue VLSI for temporal frequency analysis of visual data." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/11441.

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When viewed with an electronic imager, any variation in light intensity over time can provide valuable information regarding the nature of the object or light source causing the intensity change. By estimating the frequency of such light intensity variations, temporal frequencies can be extracted from the visual data, which may prove useful in a variety of applications. For instance, certain objects exhibit unique temporal frequencies, which could facilitate identification or classification. Other potential applications include remote, early failure detection for rotating machinery, as well as the possible detection of cancerous breasts using infra-red imaging techniques. The aim of the research reported in this thesis is the development of a CMOS image-processor, capable of extracting such temporal frequencies from any scene it is exposed to. In addition to finding the fundamental frequency, the sensor aims to extract the relative strength of up to the first four harmonics, performing a Fourier style decomposition of the incident light intensity into a temporal frequency signature. A heavy emphasis was placed on low power operation, leading to an investigation of analogue signal processing techniques with transistors biased in the subthreshold region of operation. The parallel processing advantages of combining light sensitive elements with signal processing elements in each pixel were also investigated, resulting in a system incorporating focal-plane computation. Software simulations of various novel system level algorithms are reported, with the successful approach used to create fundamental frequency maps of test data. The approach was also simulated to prove its robustness to noise commonly found in CMOS imager implementations. Circuits are presented which accurately extract the fundamental frequency of variations in light intensity, while benefiting from the low power consumption of subthreshold analogue circuitry. A novel algorithm which places a band pass filter onto the fundamental frequency of any incident light intensity with an accuracy of 3 % is also presented. The system can tune from 20 Hz to 10 kHz at a maximum rate of 9 kHz/s, and can be considered the first step in the creation of a single-chip pseudo-Fourier light intensity processing unit.

6

Wu, Elizabeth. "Spatio-Temporal Data Mining and Analysis of Precipitation Extremes." Thesis, The University of Sydney, 2008. https://hdl.handle.net/2123/28120.

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The work in this thesis aimed to discover new ways to analyse and mine information about precipitation extremes in South America from spatio-temporal data. This was done in two ways. First, analysis was performed through the use of statistical measures that provided insight into the behaviour of precipitation extremes between regions. Second, a new spatio-temporal outlier detection algorithm was introduced to discover moving outliers from the data.

7

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.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.

8

Chen, Feng. "Efficient Algorithms for Mining Large Spatio-Temporal Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19220.

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Knowledge discovery on spatio-temporal datasets has attracted
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.

9

Wang, Zilong. "Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models." UKnowledge, 2012. http://uknowledge.uky.edu/statistics_etds/3.

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Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time.

10

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.

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Книги з теми "Temporal data analysis":

1

Cressie, Noel A. C. Statistics for spatio-temporal data. Hoboken, N.J: Wiley, 2011.

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2

Hsu, Wynne. Temporal and spatio-temporal data mining. Hershey PA: Idea Group Pub., 2008.

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3

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.

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4

Sherman, Michael. Spatial statistics and spatio-temporal data: Covariance functions and directional properties. Chichester, West Sussex, U.K: Wiley, 2011.

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5

Ott, Thomas. Time-integrative geographic information systems: Management and analysis of spatio-temporal data. Berlin: Springer, 2001.

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6

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.

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7

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.

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8

Ott, Thomas. Time-Integrative Geographic Information Systems: Management and Analysis of Spatio-Temporal Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001.

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9

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.

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10

Wang, Jiaqiu. Shi kong xu lie shu ju fen xi he jian mo. 8th ed. Beijing: Ke xue chu ban she, 2012.

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Частини книг з теми "Temporal data analysis":

1

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.

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2

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.

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3

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.

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4

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.

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5

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.

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6

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.

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7

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.

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8

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.

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9

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.

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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.

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Тези доповідей конференцій з теми "Temporal data analysis":

1

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.

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2

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.

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3

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.

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4

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.

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5

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.

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6

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.

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7

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.

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8

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.

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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.

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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.

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Звіти організацій з теми "Temporal data analysis":

1

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.

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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.

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3

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.

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4

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.

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In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.

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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.

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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.

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7

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.

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This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation). In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can facilitate the development of synchronized demand response strategies and dynamic energy pricing. Moreover, the timeshifted cross-correlation analysis assists in improving forecasting models for solar power generation. By identifying significant correlations between solar generation data from different locations and applying time shifts to account for variations in weather and sunlight exposure, operators can enhance the accuracy of their predictions. This methodology enables them to fill in missing data points by leveraging correlated information from neighboring regions. Consequently, more robust forecasting models can be developed, leading to better resource allocation, improved energy management, and reduced operational uncertainties in the grid. This research highlights the evaluation and potential of utilizing spatio-temporal and temporal correlations in solar power data to enhance energy management and planning.

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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.

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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.

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Federal crop insurance provides a financial safety net for farmers against insured perils such as drought, heat, and freeze. In 2016 over $100 billion dollars of crops were insured through the Federal crop insurance program administered by the U.S. Department of Agriculture Risk Management Agency. In this white paper, we analyze publicly-available Federal crop insurance data to understand how weather and climate-related perils, or causes of loss (COL), change over time and spatial areas. We find that over 75% of all weather/climate-related indemnities (i.e., crop losses) from 2001 to 2016 are due to three COL: drought, excess moisture, and hail. However, the extent to which these top COL and others impact indemnities is highly dependent on the time period, temporal scale, and spatial scale of analysis. Moreover, we identify what COL are region- or season-specific, and visualize COL trends over time. Finally, we offer a road map of research applications to quantify such trends in indemnities, as well as outreach and extension efforts that include an online data portal.

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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.

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To promote active transportation modes (such as bike ride and walking), and to create safer communities for easier access to transit, it is essential to provide consolidated data-driven transportation information to the public. The relevant and timely information from data facilitates the improvement of decision-making processes for the establishment of public policy and urban planning for sustainable growth, and for promoting public health in the region. For the characterization of the spatial variation of transportation-emitted air pollution in the Fresno/Clovis neighborhood in California, various species of particulate matters emitted from traffic sources were measured using real-time monitors and GPS loggers at over 100 neighborhood walking routes within 58 census tracts from the previous research, Children’s Health to Air Pollution Study - San Joaquin Valley (CHAPS-SJV). Roadside air pollution data show that PM2.5, black carbon, and PAHs were significantly elevated in the neighborhood walking air samples compared to indoor air or the ambient monitoring station in the Central Fresno area due to the immediate source proximity. The simultaneous parallel measurements in two neighborhoods which are distinctively different areas (High diesel High poverty vs. Low diesel Low poverty) showed that the higher pollution levels were observed when more frequent vehicular activities were occurring around the neighborhoods. Elevated PM2.5 concentrations near the roadways were evident with a high volume of traffic and in regions with more unpaved areas. Neighborhood walking air samples were influenced by immediate roadway traffic conditions, such as encounters with diesel trucks, approaching in close proximity to freeways and/or busy roadways, passing cigarette smokers, and gardening activity. The elevated black carbon concentrations occur near the highway corridors and regions with high diesel traffic and high industry. This project provides consolidated data-driven transportation information to the public including: 1. Transportation-related particle pollution data 2. Spatial analyses of geocoded vehicle emissions 3. Neighborhood characterization for the built environment such as cities, buildings, roads, parks, walkways, etc.