Thematische Bibliographien / Temporal data analysis / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Temporal data analysis“

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Autor: Grafiati
Veröffentlicht am 7. Juli 2024

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1

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.

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2

Kyriazis, 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.

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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, Nr. 2 (06.05.2004): 123–29. http://dx.doi.org/10.1127/0941-2948/2004/0013-0123.

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4

Lesauskiene, 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.

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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.
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5

He, 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.

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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.
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6

Krzyśko, 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.

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7

Juuso, 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.

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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.
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8

Riedl, 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.

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9

Bernardeschi, 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.

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10

Naya, 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.

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11

Duman, Ali Nabi, und Ahmet E. Tatar. „Topological data analysis for revealing dynamic brain reconfiguration in MEG data“. PeerJ 11 (20.07.2023): e15721. http://dx.doi.org/10.7717/peerj.15721.

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In recent years, the focus of the functional connectivity community has shifted from stationary approaches to the ones that include temporal dynamics. Especially, non-invasive electrophysiological data (magnetoencephalography/electroencephalography (MEG/EEG)) with high temporal resolution and good spatial coverage have made it possible to measure the fast alterations in the neural activity in the brain during ongoing cognition. In this article, we analyze dynamic brain reconfiguration using MEG images collected from subjects during the rest and the cognitive tasks. Our proposed topological data analysis method, called Mapper, produces biomarkers that differentiate cognitive tasks without prior spatial and temporal collapse of the data. The suggested method provides an interactive visualization of the rapid fluctuations in electrophysiological data during motor and cognitive tasks; hence, it has the potential to extract clinically relevant information at an individual level without temporal and spatial collapse.
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H, Sudarsan Kumar Raju. „Temporal Pattern Classification and Analysis of Large Volume of Data Using Associated Data Placement Algorithm and Online Community Adjustment“. Revista Gestão Inovação e Tecnologias 11, Nr. 4 (10.07.2021): 1327–38. http://dx.doi.org/10.47059/revistageintec.v11i4.2190.

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13

Leng, Xiaoyan, und Hans-Georg Müller. „Classification using functional data analysis for temporal gene expression data“. Bioinformatics 22, Nr. 1 (27.10.2005): 68–76. http://dx.doi.org/10.1093/bioinformatics/bti742.

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14

Lee, D. J., Z. Zhu und P. Toscas. „Spatio-temporal functional data analysis for wireless sensor networks data“. Environmetrics 26, Nr. 5 (05.05.2015): 354–62. http://dx.doi.org/10.1002/env.2344.

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15

Dong, Zhenmin, und Chunfang Guo. „A Literature Review of Spatio-temporal Data Analysis“. Journal of Physics: Conference Series 1792, Nr. 1 (01.02.2021): 012056. http://dx.doi.org/10.1088/1742-6596/1792/1/012056.

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16

Tselentis, Dimitrios I., Eleni I. Vlahogianni und George Yannis. „Temporal analysis of driving efficiency using smartphone data“. Accident Analysis & Prevention 154 (Mai 2021): 106081. http://dx.doi.org/10.1016/j.aap.2021.106081.

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17

Campalani, P., A. Beccati, S. Mantovani und P. Baumann. „TEMPORAL ANALYSIS OF ATMOSPHERIC DATA USING OPEN STANDARDS“. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (23.04.2014): 21–27. http://dx.doi.org/10.5194/isprsannals-ii-4-21-2014.

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The continuous growth of remotely sensed data raises the need for efficient ways of accessing data archives. The classical model of accessing remote sensing (satellite) archives via distribution of large files is increasingly making way for a more dynamic and interactive data service. A challenge, though, is interoperability of such services, in particular when multi-dimensional data and advanced processing are involved. Individually crafted service interfaces typically do not allow substitution and combination of services. Open standards can provide a way forward if they are powerful enough to address both data and processing model. <br><br> The OGC Web Coverage Service (WCS) is a modular service suite which provides high-level interface definitions for data access, subsetting, filtering, and processing of spatio-temporal raster data. WCS based service interfaces to data archives deliver data in their original semantics useful for further client-side processing, as opposed to the Web Map Service (WMS) (de la Beaujardière, 2006) which performs a pre-rendering into images only useful for display to humans. <br><br> In this paper we present a case study where the OGC coverage data and service model defines the client/server interface for a climate data service. In particular, we show how flexible temporal analysis can be performed efficiently on massive spatio-temporal coverage objects. This service, which is operational on a several Terabyte data holding, has been established as part of the <i>EarthServer</i> initiative focusing on Big Data in the Earth and Planetary sciences.
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18

Boldt, Martin, und Anton Borg. „Evaluating Temporal Analysis Methods Using Residential Burglary Data“. ISPRS International Journal of Geo-Information 5, Nr. 9 (25.08.2016): 148. http://dx.doi.org/10.3390/ijgi5090148.

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19

Ueda, Naonori. „Spatio-temporal Data Analysis for Realizing Ambient Intelligence“. IEICE Communications Society Magazine 12, Nr. 1 (01.06.2018): 21–28. http://dx.doi.org/10.1587/bplus.12.21.

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20

Jackle, Dominik, Fabian Fischer, Tobias Schreck und Daniel A. Keim. „Temporal MDS Plots for Analysis of Multivariate Data“. IEEE Transactions on Visualization and Computer Graphics 22, Nr. 1 (31.01.2016): 141–50. http://dx.doi.org/10.1109/tvcg.2015.2467553.

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21

Garg, Nek R., und George V. Keller. „Spatial and temporal analysis of electromagnetic survey data“. GEOPHYSICS 51, Nr. 1 (Januar 1986): 85–89. http://dx.doi.org/10.1190/1.1442042.

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Development of a relatively straightforward approach to interpretation of electromagnetic survey data when the earth in the vicinity of the survey has a complex geoelectric structure will be necessary before such methods can assume their full role in geophysical exploration. One‐dimensional interpretation methods have been well developed to extract the resistivity‐depth profile from a transient electromagnetic (TEM) sounding when the earth is assumed to be simply layered. Extension of the same methods to more complicated earth structures is difficult because of the tedious calculations involved when three‐dimensional earth structures are examined. An alternate approach could be use of the information contained in the spatial spectra of a set of sounding measurements. In such an approach, it should be possible to obtain a clearer concept of the geoelectric structure by analytic continuation of the electromagnetic field in space, or by heuristic filtering of the field, as is done in various potential field methods in geophysics. To try this concept, a filtering technique developed for treating magnetic data was applied to a set of TEM data acquired in the Snake River plain of Idaho. The results are reasonable, but insufficient control information is available to prove their significance. The effort has demonstrated that such a filtering approach can be done quickly, but it places demands on how the field data are sampled in the space domain.
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22

McGaughey, Donald R., und George J. M. Aitken. „Temporal analysis of stellar wave-front-tilt data“. Journal of the Optical Society of America A 14, Nr. 8 (01.08.1997): 1967. http://dx.doi.org/10.1364/josaa.14.001967.

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23

Mijović, Vuk, Valentina Janev, Dejan Paunović und Sanja Vraneš. „Exploratory spatio-temporal analysis of linked statistical data“. Journal of Web Semantics 41 (Dezember 2016): 1–8. http://dx.doi.org/10.1016/j.websem.2016.10.002.

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24

Afonso, Ana Paula, Maria Beatriz Carmo, Tiago Gonçalves und Pedro Vieira. „VisuaLeague: Player performance analysis using spatial-temporal data“. Multimedia Tools and Applications 78, Nr. 23 (11.07.2019): 33069–90. http://dx.doi.org/10.1007/s11042-019-07952-z.

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25

Su, Han, Kai Zheng, Jiamin Huang, Haozhou Wang und Xiaofang Zhou. „Calibrating trajectory data for spatio-temporal similarity analysis“. VLDB Journal 24, Nr. 1 (05.07.2014): 93–116. http://dx.doi.org/10.1007/s00778-014-0365-y.

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26

Sudhira, H. S., T. V. Ramachandra, Karthik S. Raj und K. S. Jagadish. „Urban growth analysis using spatial and temporal data“. Journal of the Indian Society of Remote Sensing 31, Nr. 4 (Dezember 2003): 299–311. http://dx.doi.org/10.1007/bf03007350.

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27

Ritzi, Robert W., Stephen L. Wright, Barbara Mann und Manling Chen. „Analysis of Temporal Variability in Hydrogeochemical Data Used for Multivariate Analyses“. Ground Water 31, Nr. 2 (März 1993): 221–29. http://dx.doi.org/10.1111/j.1745-6584.1993.tb01814.x.

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28

김정희. „Exploratory Analysis and Visualization of Spatio-Temporal Data Using Data Mining“. Journal of the Association of Korean Photo-Geographers 29, Nr. 4 (Dezember 2019): 143–58. http://dx.doi.org/10.35149/jakpg.2019.29.4.011.

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29

Baumgartner, Richard, Lawrence Ryner, Ray Somorjai und Randy Summers. „Exploratory data analysis reveals spatio-temporal structure of null fMRI data“. NeuroImage 11, Nr. 5 (Mai 2000): S639. http://dx.doi.org/10.1016/s1053-8119(00)91569-6.

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30

Suhaila, Jamaludin, und Zulkifli Yusop. „Spatial and temporal variabilities of rainfall data using functional data analysis“. Theoretical and Applied Climatology 129, Nr. 1-2 (29.03.2016): 229–42. http://dx.doi.org/10.1007/s00704-016-1778-x.

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31

Peltier, Caroline, Michel Visalli, Pascal Schlich und Hervé Cardot. „Analyzing temporal dominance of sensations data with categorical functional data analysis“. Food Quality and Preference 109 (Juli 2023): 104893. http://dx.doi.org/10.1016/j.foodqual.2023.104893.

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32

Karaliutė, Marta, und Kęstutis Dučinskas. „Classification of Gaussian spatio-temporal data with stationary separable covariances“. Nonlinear Analysis: Modelling and Control 26, Nr. 2 (01.03.2021): 363–74. http://dx.doi.org/10.15388/namc.2021.26.22359.

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The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes specified by different drift parameters, separable nonlinear covariance functions and nonstationary label field. The performance of proposed classification rule is validated by the values of local Bayes and empirical error rates realized by leave one out procedure. A simulation study for spatial covariance functions belonging to powered-exponential family and temporal covariance functions of AR(1) models is carried out. The influence of the values of spatial and temporal covariance parameters to error rates for several label field models are studied. The results showed that the proposed classification methodology can be applied successfully in practice with small error rates and can be a useful tool for discriminant analysis of spatio-temporal data.
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33

Zyphur, Michael J., Manuel C. Voelkle, Louis Tay, Paul D. Allison, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker et al. „From Data to Causes II: Comparing Approaches to Panel Data Analysis“. Organizational Research Methods 23, Nr. 4 (24.05.2019): 688–716. http://dx.doi.org/10.1177/1094428119847280.

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This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
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34

Rodenacker, Karsten, Klaus Hahn, Gerhard Winkler und Dorothea P. Auer. „SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA“. Image Analysis & Stereology 19, Nr. 3 (03.05.2011): 189. http://dx.doi.org/10.5566/ias.v19.p189-194.

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Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.
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Zhao, Ling, Hanhan Deng, Linyao Qiu, Sumin Li, Zhixiang Hou, Hai Sun und Yun Chen. „Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding“. Symmetry 12, Nr. 2 (01.02.2020): 199. http://dx.doi.org/10.3390/sym12020199.

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.
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Ma, Ping, Wenxuan Zhong, Yang Feng und Jun S. Liu. „Bayesian Functional Data Clustering for Temporal Microarray Data“. International Journal of Plant Genomics 2008 (17.04.2008): 1–4. http://dx.doi.org/10.1155/2008/231897.

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We propose a Bayesian procedure to cluster temporal gene expression microarray profiles, based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from the desired posterior distribution. Our method can determine the cluster number automatically based on the Bayesian information criterion, and handle missing data easily. When applied to a microarray dataset on the budding yeast, our clustering algorithm provides biologically meaningful gene clusters according to a functional enrichment analysis.
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Prevost, Paoline, Kristel Chanard, Luce Fleitout, Eric Calais, Damian Walwer, Tonie van Dam und Michael Ghil. „Data-adaptive spatio-temporal filtering of GRACE data“. Geophysical Journal International 219, Nr. 3 (19.09.2019): 2034–55. http://dx.doi.org/10.1093/gji/ggz409.

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SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.
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38

Shenk, Kimberly, Dimitris Bertsimas und Natasha Markuzon. „Semi-Supervised Approach to Predictive Analysis Using Temporal Data“. Military Operations Research 19, Nr. 1 (01.03.2014): 37–50. http://dx.doi.org/10.5711/1082598319137.

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39

Vinciotti, Veronica, und Keming Yu. „M-quantile Regression Analysis of Temporal Gene Expression Data“. Statistical Applications in Genetics and Molecular Biology 8, Nr. 1 (22.01.2009): 1–20. http://dx.doi.org/10.2202/1544-6115.1452.

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40

Chang, K. T., H. M. Fang, S. S. Hsiao und C. S. Li. „Beach Topographic Change Analysis Using Multi-temporal UAV Data“. IOP Conference Series: Earth and Environmental Science 799, Nr. 1 (01.06.2021): 012022. http://dx.doi.org/10.1088/1755-1315/799/1/012022.

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41

Zhicheng Liu, J. Stasko und T. Sullivan. „SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data“. IEEE Transactions on Visualization and Computer Graphics 15, Nr. 6 (November 2009): 1025–32. http://dx.doi.org/10.1109/tvcg.2009.180.

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42

Frye, George J. „High‐definition temporal/frequency analysis of sampled data sets“. Journal of the Acoustical Society of America 102, Nr. 5 (November 1997): 3145. http://dx.doi.org/10.1121/1.420688.

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43

Doraiswamy, Harish, Juliana Freire, Marcos Lage, Fabio Miranda und Claudio Silva. „Spatio-Temporal Urban Data Analysis: A Visual Analytics Perspective“. IEEE Computer Graphics and Applications 38, Nr. 5 (September 2018): 26–35. http://dx.doi.org/10.1109/mcg.2018.053491728.

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44

Liebig, Thomas, Gennady Andrienko und Natalia Andrienko. „Methods for Analysis of Spatio-Temporal Bluetooth Tracking Data“. Journal of Urban Technology 21, Nr. 2 (03.04.2014): 27–37. http://dx.doi.org/10.1080/10630732.2014.888215.

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45

Diggle, Peter J., Irene Kaimi und Rosa Abellana. „Partial-Likelihood Analysis of Spatio-Temporal Point-Process Data“. Biometrics 66, Nr. 2 (10.08.2009): 347–54. http://dx.doi.org/10.1111/j.1541-0420.2009.01304.x.

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46

Faddy, M. F., und M. C. Jones. „Modelling and Analysis of Data that Exhibit Temporal Decay“. Journal of the Royal Statistical Society: Series C (Applied Statistics) 48, Nr. 2 (Januar 1999): 229–37. http://dx.doi.org/10.1111/1467-9876.00151.

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47

De Mazière, P. A., und M. M. Van Hulle. „Towards a spatio-temporal analysis tool for fMRI data“. NeuroImage 11, Nr. 5 (Mai 2000): S617. http://dx.doi.org/10.1016/s1053-8119(00)91547-7.

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48

Woolrich, Mark, Michael Brady und Stephen M. Smith. „Hierarchical fully bayesian spatio-temporal analysis of FMRI data“. NeuroImage 13, Nr. 6 (Juni 2001): 287. http://dx.doi.org/10.1016/s1053-8119(01)91630-1.

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49

DOMINGUES, A. R., A. R. VIEIRA, R. S. HENDRIKSEN, C. PULSRIKARN und F. M. AARESTRUP. „Spatio-temporal analysis of Salmonella surveillance data in Thailand“. Epidemiology and Infection 142, Nr. 8 (09.10.2013): 1614–24. http://dx.doi.org/10.1017/s095026881300215x.

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SUMMARYThis study evaluates the usefulness of spatio-temporal statistical tools to detect outbreaks using routine surveillance data where limited epidemiological information is available. A dataset from 2002 to 2007 containing information regarding date, origin, source and serotype of 29 586 Salmonella isolates from Thailand was analysed. Data was grouped into human and non-human categories and the analysis was performed for the top five occurring serovars for each year of the study period. A total 91 human and 39 non-human significant spatio-temporal clusters were observed, accounting for 11% and 16% of the isolates, respectively. Serovar-specific associations between human and non-human clusters were also evaluated. Results show that these statistical tools can provide information for use in outbreak prevention and detection, in countries where only limited data is available. Moreover, it is suggested that monitoring non-human reservoirs can be relevant in predicting future Salmonella human cases.
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50

Grignetti, A., R. Salvatori, R. Casacchia und F. Manes. „Mediterranean vegetation analysis by multi-temporal satellite sensor data“. International Journal of Remote Sensing 18, Nr. 6 (April 1997): 1307–18. http://dx.doi.org/10.1080/014311697218430.

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