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You searched for subject:(spatio temporal prediction). Showing records 1 – 19 of 19 total matches.

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Virginia Tech

1. Chen, Feng. Efficient Algorithms for Mining Large Spatio-Temporal Data.

Degree: PhD, Computer Science, 2013, Virginia Tech

 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,… (more)

Subjects/Keywords: Spatio-Temporal Analysis; Outlier Detection; Robust Prediction; Energy Disaggregation

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APA (6th Edition):

Chen, F. (2013). Efficient Algorithms for Mining Large Spatio-Temporal Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/19220

Chicago Manual of Style (16th Edition):

Chen, Feng. “Efficient Algorithms for Mining Large Spatio-Temporal Data.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2020. http://hdl.handle.net/10919/19220.

MLA Handbook (7th Edition):

Chen, Feng. “Efficient Algorithms for Mining Large Spatio-Temporal Data.” 2013. Web. 22 Jan 2020.

Vancouver:

Chen F. Efficient Algorithms for Mining Large Spatio-Temporal Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10919/19220.

Council of Science Editors:

Chen F. Efficient Algorithms for Mining Large Spatio-Temporal Data. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/19220


The Ohio State University

2. Agarwal, Abhijat. A New Approach to Spatio-Temporal Kriging and Its Applications.

Degree: MS, Computer Science and Engineering, 2011, The Ohio State University

  Stochastic spatio-temporal variability is often observed in naturally occurring phenomena. It had always been a challenge to predict their behavior in space and time.… (more)

Subjects/Keywords: Climate Change; Computer Science; Geography; Statistics; Spatio-Temporal; Universal Kriging; ARIMA; Prediction

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APA (6th Edition):

Agarwal, A. (2011). A New Approach to Spatio-Temporal Kriging and Its Applications. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1306871646

Chicago Manual of Style (16th Edition):

Agarwal, Abhijat. “A New Approach to Spatio-Temporal Kriging and Its Applications.” 2011. Masters Thesis, The Ohio State University. Accessed January 22, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306871646.

MLA Handbook (7th Edition):

Agarwal, Abhijat. “A New Approach to Spatio-Temporal Kriging and Its Applications.” 2011. Web. 22 Jan 2020.

Vancouver:

Agarwal A. A New Approach to Spatio-Temporal Kriging and Its Applications. [Internet] [Masters thesis]. The Ohio State University; 2011. [cited 2020 Jan 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1306871646.

Council of Science Editors:

Agarwal A. A New Approach to Spatio-Temporal Kriging and Its Applications. [Masters Thesis]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1306871646


Virginia Tech

3. Chen, Yang. Robust Prediction of Large Spatio-Temporal Datasets.

Degree: MS, Computer Science, 2013, Virginia Tech

 This thesis describes a robust and efficient design of Student-t based Robust Spatio-Temporal Prediction, namely, St-RSTP, to provide estimation based on observations over spatio-temporal neighbors.… (more)

Subjects/Keywords: Robust Prediction; Expectation Propagation; Student's t Model; Bayesian Hierarchical Model; Spatio-Temporal Process

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APA (6th Edition):

Chen, Y. (2013). Robust Prediction of Large Spatio-Temporal Datasets. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/23098

Chicago Manual of Style (16th Edition):

Chen, Yang. “Robust Prediction of Large Spatio-Temporal Datasets.” 2013. Masters Thesis, Virginia Tech. Accessed January 22, 2020. http://hdl.handle.net/10919/23098.

MLA Handbook (7th Edition):

Chen, Yang. “Robust Prediction of Large Spatio-Temporal Datasets.” 2013. Web. 22 Jan 2020.

Vancouver:

Chen Y. Robust Prediction of Large Spatio-Temporal Datasets. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10919/23098.

Council of Science Editors:

Chen Y. Robust Prediction of Large Spatio-Temporal Datasets. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/23098


Kansas State University

4. Chowdhury, Sohini Roy. Mathematical models for prediction and optimal mitigation of epidemics.

Degree: MS, Department of Electrical and Computer Engineering, 2010, Kansas State University

 Early detection of livestock diseases and development of cost optimal mitigation strategies are becoming a global necessity. Foot and Mouth Disease (FMD) is considered one… (more)

Subjects/Keywords: spatio-temporal; prediction; mitigation; meta-population; epidemic; Engineering, Electronics and Electrical (0544)

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APA (6th Edition):

Chowdhury, S. R. (2010). Mathematical models for prediction and optimal mitigation of epidemics. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/3874

Chicago Manual of Style (16th Edition):

Chowdhury, Sohini Roy. “Mathematical models for prediction and optimal mitigation of epidemics.” 2010. Masters Thesis, Kansas State University. Accessed January 22, 2020. http://hdl.handle.net/2097/3874.

MLA Handbook (7th Edition):

Chowdhury, Sohini Roy. “Mathematical models for prediction and optimal mitigation of epidemics.” 2010. Web. 22 Jan 2020.

Vancouver:

Chowdhury SR. Mathematical models for prediction and optimal mitigation of epidemics. [Internet] [Masters thesis]. Kansas State University; 2010. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/2097/3874.

Council of Science Editors:

Chowdhury SR. Mathematical models for prediction and optimal mitigation of epidemics. [Masters Thesis]. Kansas State University; 2010. Available from: http://hdl.handle.net/2097/3874


RMIT University

5. Sadri, A. Mining human mobility patterns from pervasive spatial and temporal data.

Degree: 2018, RMIT University

 Recent advances in communication, sensors and processors have made pervasive systems more computationally powerful and increasingly popular. These systems are deployed everywhere all the time… (more)

Subjects/Keywords: Fields of Research; Pervasive signals; Spatio-temporal data; Trajectory prediction; Graph compression; Temporal segmentation; Time series analysis

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APA (6th Edition):

Sadri, A. (2018). Mining human mobility patterns from pervasive spatial and temporal data. (Thesis). RMIT University. Retrieved from http://researchbank.rmit.edu.au/view/rmit:162474

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sadri, A. “Mining human mobility patterns from pervasive spatial and temporal data.” 2018. Thesis, RMIT University. Accessed January 22, 2020. http://researchbank.rmit.edu.au/view/rmit:162474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sadri, A. “Mining human mobility patterns from pervasive spatial and temporal data.” 2018. Web. 22 Jan 2020.

Vancouver:

Sadri A. Mining human mobility patterns from pervasive spatial and temporal data. [Internet] [Thesis]. RMIT University; 2018. [cited 2020 Jan 22]. Available from: http://researchbank.rmit.edu.au/view/rmit:162474.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sadri A. Mining human mobility patterns from pervasive spatial and temporal data. [Thesis]. RMIT University; 2018. Available from: http://researchbank.rmit.edu.au/view/rmit:162474

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade do Rio Grande do Sul

6. Pinto, Rafael Coimbra. Online incremental one-shot learning of temporal sequences.

Degree: 2011, Universidade do Rio Grande do Sul

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo… (more)

Subjects/Keywords: Neural networks; Inteligência artificial; Redes neurais; Spatio-temporal pattern processing; Gaussian mixtures; Reservoir computing; Time-series prediction

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APA (6th Edition):

Pinto, R. C. (2011). Online incremental one-shot learning of temporal sequences. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/49063

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Pinto, Rafael Coimbra. “Online incremental one-shot learning of temporal sequences.” 2011. Thesis, Universidade do Rio Grande do Sul. Accessed January 22, 2020. http://hdl.handle.net/10183/49063.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Pinto, Rafael Coimbra. “Online incremental one-shot learning of temporal sequences.” 2011. Web. 22 Jan 2020.

Vancouver:

Pinto RC. Online incremental one-shot learning of temporal sequences. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2011. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10183/49063.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pinto RC. Online incremental one-shot learning of temporal sequences. [Thesis]. Universidade do Rio Grande do Sul; 2011. Available from: http://hdl.handle.net/10183/49063

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

7. Lundberg, Emil. Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion.

Degree: Computer Science and Communication (CSC), 2015, KTH

Vector Quantization (VQ) is a classic optimization problem and a simple approach to pattern recognition. Applications include lossy data compression, clustering and speech and… (more)

Subjects/Keywords: ANN; artificial neural network; SOINN; SOTPAR; SOTPAR2; prediction; spatio-temporal pattern detection; temporal activity diffusion; pattern recognition; unsupervised learning; vector quantization; ANN; artificiellt neuralt nätverk; artificiella neurala nätverk; SOINN; SOTPAR; SOTPAR2; förutsägelse; spatio-temporal mönsterdetekion; temporal aktivitetsdiffusion; mönsterigenkänning; oövervakad inlärning; vektorkvantisering; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Lundberg, E. (2015). Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lundberg, Emil. “Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion.” 2015. Thesis, KTH. Accessed January 22, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lundberg, Emil. “Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion.” 2015. Web. 22 Jan 2020.

Vancouver:

Lundberg E. Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion. [Internet] [Thesis]. KTH; 2015. [cited 2020 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lundberg E. Adding temporal plasticity to a self-organizing incremental neural network using temporal activity diffusion. [Thesis]. KTH; 2015. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-180346

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Faghmous, James Hocine. Understanding climate change and Variability III: a spatio-temporal data mining perspective.

Degree: PhD, Computer Science, 2013, University of Minnesota

Subjects/Keywords: Climate change; data mining; Hurricane prediction; Ocean eddies; Spatio-temporal; Spatio-temporal data mining

…pattern mining. In both instances, we show that insightfully mining the spatio-temporal context… …algorithms. We focus on two spatio-temporal data mining applications one predicting Atlantic… …are able to leverage the spatio-temporal context of the data to identify more physically… …Advances in STDM applications to Climate 12 3.1 Spatio-Temporal Query Matching… …Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 v 4 Spatio-Temporal Data… 

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APA (6th Edition):

Faghmous, J. H. (2013). Understanding climate change and Variability III: a spatio-temporal data mining perspective. (Doctoral Dissertation). University of Minnesota. Retrieved from http://purl.umn.edu/153261

Chicago Manual of Style (16th Edition):

Faghmous, James Hocine. “Understanding climate change and Variability III: a spatio-temporal data mining perspective.” 2013. Doctoral Dissertation, University of Minnesota. Accessed January 22, 2020. http://purl.umn.edu/153261.

MLA Handbook (7th Edition):

Faghmous, James Hocine. “Understanding climate change and Variability III: a spatio-temporal data mining perspective.” 2013. Web. 22 Jan 2020.

Vancouver:

Faghmous JH. Understanding climate change and Variability III: a spatio-temporal data mining perspective. [Internet] [Doctoral dissertation]. University of Minnesota; 2013. [cited 2020 Jan 22]. Available from: http://purl.umn.edu/153261.

Council of Science Editors:

Faghmous JH. Understanding climate change and Variability III: a spatio-temporal data mining perspective. [Doctoral Dissertation]. University of Minnesota; 2013. Available from: http://purl.umn.edu/153261

9. Faye, Papa Abdoulaye. Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data.

Degree: Docteur es, Mathématiques Appliquées, 2015, Université Blaise-Pascale, Clermont-Ferrand II

La Modélisation spatio-temporelle permet la prédiction d’une variable régionalisée à des sites non observés du domaine d’étude, basée sur l’observation de cette variable en quelques… (more)

Subjects/Keywords: Prédiction spatio-temporelle; Bayésien; Information spatiale; Information temporelle; Boîte noire; A priori; Plans d’expérience; Critère d’optimalité; Spatio-temporal prediction; Bayesian; Spatial information; Temporal information; Black-box; Prior information; Experimental design; Optimality criterion

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APA (6th Edition):

Faye, P. A. (2015). Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2015CLF22638

Chicago Manual of Style (16th Edition):

Faye, Papa Abdoulaye. “Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data.” 2015. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed January 22, 2020. http://www.theses.fr/2015CLF22638.

MLA Handbook (7th Edition):

Faye, Papa Abdoulaye. “Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data.” 2015. Web. 22 Jan 2020.

Vancouver:

Faye PA. Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. [cited 2020 Jan 22]. Available from: http://www.theses.fr/2015CLF22638.

Council of Science Editors:

Faye PA. Planification et analyse de données spatio-temporelles : Design and analysis of spatio-temporal data. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. Available from: http://www.theses.fr/2015CLF22638


Indian Institute of Science

10. Suryawanshi, Anup Arvind. Uncertainty Quantification in Flow and Flow Induced Structural Response.

Degree: 2015, Indian Institute of Science

 Response of flexible structures — such as cable-supported bridges and aircraft wings — is associated with a number of uncertainties in structural and flow parameters.… (more)

Subjects/Keywords: Flexible Structures; Structural Uncertainty Quantification; Spatio-temporal Random Process; Computational Mechanics; Wind Speed Prediction; Limit Cycle Oscillations (LCOs); Spatio-temporal Covariance; Flow and Flow Induced Structural Response; Structural Stability; Polynomial Chaos; Hybrid Sampling Technique; Aeroelasticity; Aeroelastic Stability; Civil Engineering

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APA (6th Edition):

Suryawanshi, A. A. (2015). Uncertainty Quantification in Flow and Flow Induced Structural Response. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/2005/3875 ; http://etd.iisc.ernet.in/abstracts/4747/G26886-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Suryawanshi, Anup Arvind. “Uncertainty Quantification in Flow and Flow Induced Structural Response.” 2015. Thesis, Indian Institute of Science. Accessed January 22, 2020. http://etd.iisc.ernet.in/2005/3875 ; http://etd.iisc.ernet.in/abstracts/4747/G26886-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Suryawanshi, Anup Arvind. “Uncertainty Quantification in Flow and Flow Induced Structural Response.” 2015. Web. 22 Jan 2020.

Vancouver:

Suryawanshi AA. Uncertainty Quantification in Flow and Flow Induced Structural Response. [Internet] [Thesis]. Indian Institute of Science; 2015. [cited 2020 Jan 22]. Available from: http://etd.iisc.ernet.in/2005/3875 ; http://etd.iisc.ernet.in/abstracts/4747/G26886-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Suryawanshi AA. Uncertainty Quantification in Flow and Flow Induced Structural Response. [Thesis]. Indian Institute of Science; 2015. Available from: http://etd.iisc.ernet.in/2005/3875 ; http://etd.iisc.ernet.in/abstracts/4747/G26886-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Tech

11. Duan, Yuanyuan. Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data.

Degree: PhD, Statistics, 2014, Virginia Tech

 This dissertation aims to develop methods for statistical predictions based on various types of data from different areas. We focus on applications from reliability and… (more)

Subjects/Keywords: Coatings; Covariate process; Clusters; Divide-Recombine; Environmental conditions; Lifetime prediction; Lyme disease; Kernel smoothing; Photodegradation; Usage history; UV exposure; Random effects; Reliability; Spatio-temporal.

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APA (6th Edition):

Duan, Y. (2014). Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/56616

Chicago Manual of Style (16th Edition):

Duan, Yuanyuan. “Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 22, 2020. http://hdl.handle.net/10919/56616.

MLA Handbook (7th Edition):

Duan, Yuanyuan. “Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data.” 2014. Web. 22 Jan 2020.

Vancouver:

Duan Y. Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/10919/56616.

Council of Science Editors:

Duan Y. Statistical Predictions Based on Accelerated Degradation Data and Spatial Count Data. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/56616

12. ZHOU JINGBO. Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis.

Degree: 2014, National University of Singapore

Subjects/Keywords: semi-lazy learning; spatio-temporal data analysis; dynamic prediction; trajectory; time series; itinerary

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APA (6th Edition):

JINGBO, Z. (2014). Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/118274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

JINGBO, ZHOU. “Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis.” 2014. Thesis, National University of Singapore. Accessed January 22, 2020. http://scholarbank.nus.edu.sg/handle/10635/118274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

JINGBO, ZHOU. “Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis.” 2014. Web. 22 Jan 2020.

Vancouver:

JINGBO Z. Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis. [Internet] [Thesis]. National University of Singapore; 2014. [cited 2020 Jan 22]. Available from: http://scholarbank.nus.edu.sg/handle/10635/118274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

JINGBO Z. Semi-Lazy Learning Approach to Dynamic Spatio-Temporal Data Analysis. [Thesis]. National University of Singapore; 2014. Available from: http://scholarbank.nus.edu.sg/handle/10635/118274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


KTH

13. Gebresilassie, Mesele Atsbeha. Spatio-temporal Traffic Flow Prediction.

Degree: Geoinformatics, 2017, KTH

  The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and… (more)

Subjects/Keywords: ITS; principal component analysis; spatio-temporal traffic flow; spatially weighted regression; traffic flow prediction; support vector machine for regression; Engineering and Technology; Teknik och teknologier

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APA (6th Edition):

Gebresilassie, M. A. (2017). Spatio-temporal Traffic Flow Prediction. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gebresilassie, Mesele Atsbeha. “Spatio-temporal Traffic Flow Prediction.” 2017. Thesis, KTH. Accessed January 22, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gebresilassie, Mesele Atsbeha. “Spatio-temporal Traffic Flow Prediction.” 2017. Web. 22 Jan 2020.

Vancouver:

Gebresilassie MA. Spatio-temporal Traffic Flow Prediction. [Internet] [Thesis]. KTH; 2017. [cited 2020 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gebresilassie MA. Spatio-temporal Traffic Flow Prediction. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

14. Tiger, Mattias. Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework.

Degree: The Institute of Technology, 2014, Linköping UniversityLinköping University

Learning to recognize and predict common activities, performed by objects and observed by sensors, is an important and challenging problem related both to artificial… (more)

Subjects/Keywords: Activity learning; Activity recognition; Activity prediction; Unsupervised On-line learning; Artificial Intelligence; Spatio-temporal; Stream processing; Sparse Gaussian process; Computer Sciences; Datavetenskap (datalogi)

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APA (6th Edition):

Tiger, M. (2014). Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework. (Thesis). Linköping UniversityLinköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tiger, Mattias. “Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework.” 2014. Thesis, Linköping UniversityLinköping University. Accessed January 22, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tiger, Mattias. “Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework.” 2014. Web. 22 Jan 2020.

Vancouver:

Tiger M. Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework. [Internet] [Thesis]. Linköping UniversityLinköping University; 2014. [cited 2020 Jan 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111648.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tiger M. Unsupervised Spatio-Temporal Activity Learning and Recognition in a Stream Processing Framework. [Thesis]. Linköping UniversityLinköping University; 2014. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-111648

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Kaunas University of Technology

15. Vlasova, Julija. Spatio-temporal analysis of wind power prediction errors.

Degree: Master, Mathematics, 2007, Kaunas University of Technology

Nowadays there is no need to convince anyone about the necessity of renewable energy. One of the most promising ways to obtain it is the… (more)

Subjects/Keywords: Wind power prediction; Spatio-temporal error analysis; WPPT; Vėjo galios prognozavimas; WPPT; Paklaidų analizė

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APA (6th Edition):

Vlasova, Julija. (2007). Spatio-temporal analysis of wind power prediction errors. (Masters Thesis). Kaunas University of Technology. Retrieved from http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142259-79654 ;

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Vlasova, Julija. “Spatio-temporal analysis of wind power prediction errors.” 2007. Masters Thesis, Kaunas University of Technology. Accessed January 22, 2020. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142259-79654 ;.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Vlasova, Julija. “Spatio-temporal analysis of wind power prediction errors.” 2007. Web. 22 Jan 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Vlasova, Julija. Spatio-temporal analysis of wind power prediction errors. [Internet] [Masters thesis]. Kaunas University of Technology; 2007. [cited 2020 Jan 22]. Available from: http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142259-79654 ;.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Vlasova, Julija. Spatio-temporal analysis of wind power prediction errors. [Masters Thesis]. Kaunas University of Technology; 2007. Available from: http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_142259-79654 ;

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

16. Cohen, Zachary Gideon. Noise Reduction with Microphone Arrays for Speaker Identification.

Degree: MS, Electrical Engineering, 2012, Cal Poly

  The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce… (more)

Subjects/Keywords: beamforming; Wiener; Spatio-Temporal Prediction; multichannel; algorithm; filter; Signal Processing; Systems and Communications

…algorithms explored are the Multichannel Wiener filter and the Spatio-Temporal Prediction filter… …4.1 New Problem Description The Multichannel Wiener filter and Spatio-Temporal Prediction… 

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APA (6th Edition):

Cohen, Z. G. (2012). Noise Reduction with Microphone Arrays for Speaker Identification. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/884 ; 10.15368/theses.2012.206

Chicago Manual of Style (16th Edition):

Cohen, Zachary Gideon. “Noise Reduction with Microphone Arrays for Speaker Identification.” 2012. Masters Thesis, Cal Poly. Accessed January 22, 2020. https://digitalcommons.calpoly.edu/theses/884 ; 10.15368/theses.2012.206.

MLA Handbook (7th Edition):

Cohen, Zachary Gideon. “Noise Reduction with Microphone Arrays for Speaker Identification.” 2012. Web. 22 Jan 2020.

Vancouver:

Cohen ZG. Noise Reduction with Microphone Arrays for Speaker Identification. [Internet] [Masters thesis]. Cal Poly; 2012. [cited 2020 Jan 22]. Available from: https://digitalcommons.calpoly.edu/theses/884 ; 10.15368/theses.2012.206.

Council of Science Editors:

Cohen ZG. Noise Reduction with Microphone Arrays for Speaker Identification. [Masters Thesis]. Cal Poly; 2012. Available from: https://digitalcommons.calpoly.edu/theses/884 ; 10.15368/theses.2012.206


Erasmus University Rotterdam

17. Broersen, Robin. Timing in the cerebellum during motor learning: from neuron to athlete to patient.

Degree: Department of Neuroscience, 2019, Erasmus University Rotterdam

 textabstractThe cerebellum is involved in the encoding and integration of spatial and temporal information. Although these processes are crucial to our survival, the neuronal mechanisms… (more)

Subjects/Keywords: cerebellum; cerebellar nuclei; perineuronal nets; whole-cell recording; eyeblink conditioning; mossy fibers; associative learning; timing; temporal; spatio-temporal prediction; spatiotemporal prediction; spinocerebellar ataxia type 6; SCA6; trajectory prediction; baseball athlete; action perception; action observation

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APA (6th Edition):

Broersen, R. (2019). Timing in the cerebellum during motor learning: from neuron to athlete to patient. (Doctoral Dissertation). Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/118729

Chicago Manual of Style (16th Edition):

Broersen, Robin. “Timing in the cerebellum during motor learning: from neuron to athlete to patient.” 2019. Doctoral Dissertation, Erasmus University Rotterdam. Accessed January 22, 2020. http://hdl.handle.net/1765/118729.

MLA Handbook (7th Edition):

Broersen, Robin. “Timing in the cerebellum during motor learning: from neuron to athlete to patient.” 2019. Web. 22 Jan 2020.

Vancouver:

Broersen R. Timing in the cerebellum during motor learning: from neuron to athlete to patient. [Internet] [Doctoral dissertation]. Erasmus University Rotterdam; 2019. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/1765/118729.

Council of Science Editors:

Broersen R. Timing in the cerebellum during motor learning: from neuron to athlete to patient. [Doctoral Dissertation]. Erasmus University Rotterdam; 2019. Available from: http://hdl.handle.net/1765/118729

18. Robert, Sylvain. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.

Degree: 2017, ETH Zürich

 Data assimilation consists in estimating the state of a system, for example the atmosphere in numerical weather prediction (NWP), by combining information coming from the… (more)

Subjects/Keywords: ensemble Kalman filter; particle filter; high-dimensional filtering; DATA ASSIMILATION/NUMERICAL WEATHER PREDICTION (METEOROLOGY); CONVECTIVE PRECIPITATION SYSTEMS + THUNDERSTORMS, SHOWERS (METEOROLOGY); Weather forecast; Spatio-temporal data; STATISTICAL ANALYSIS AND INFERENCE METHODS (MATHEMATICAL STATISTICS); STATISTICAL COMPUTATION METHODS/METEOROLOGY; ESTIMATION OF PARAMETERS AND STATE ESTIMATION (MATHEMATICAL STATISTICS); KALMAN FILTERING (CONTROL SYSTEMS THEORY); STATE SPACE METHOD (CONTROL SYSTEMS THEORY)

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APA (6th Edition):

Robert, S. (2017). Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/184084

Chicago Manual of Style (16th Edition):

Robert, Sylvain. “Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.” 2017. Doctoral Dissertation, ETH Zürich. Accessed January 22, 2020. http://hdl.handle.net/20.500.11850/184084.

MLA Handbook (7th Edition):

Robert, Sylvain. “Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation.” 2017. Web. 22 Jan 2020.

Vancouver:

Robert S. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/20.500.11850/184084.

Council of Science Editors:

Robert S. Ensemble Kalman Particle Filters for High-Dimensional Data Assimilation. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/184084

19. Ling, Esther P. An Operator-Theoretic Approach for Traffic Prediction.

Degree: MS, Electrical and Computer Engineering, 2018, Georgia Tech

 In this thesis, we develop a case for data-driven modeling of traffic flow at signalized intersections using an operator-theoretic framework. Traffic at signalized arterials is… (more)

Subjects/Keywords: Dynamic mode decomposition; Koopman operator; Signalized traffic; Traffic prediction; Spatio-temporal analysis; Traffic instability; Data driven algorithm

…understanding spatio-temporal relationships and instability occurrences. The remainder of the thesis… …we demonstrate usefulness of DMD to analyze spatio-temporal dependencies in the traffic… …complex systems into individual spatio-temporal modes 11 oscillating at different frequencies… …72 viii LIST OF TABLES 4.1 Queue Prediction Performance for New Day (l-1 norm… …of snapshots or observations r rank-truncation in the SVD T prediction horizon U left… 

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APA (6th Edition):

Ling, E. P. (2018). An Operator-Theoretic Approach for Traffic Prediction. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61167

Chicago Manual of Style (16th Edition):

Ling, Esther P. “An Operator-Theoretic Approach for Traffic Prediction.” 2018. Masters Thesis, Georgia Tech. Accessed January 22, 2020. http://hdl.handle.net/1853/61167.

MLA Handbook (7th Edition):

Ling, Esther P. “An Operator-Theoretic Approach for Traffic Prediction.” 2018. Web. 22 Jan 2020.

Vancouver:

Ling EP. An Operator-Theoretic Approach for Traffic Prediction. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2020 Jan 22]. Available from: http://hdl.handle.net/1853/61167.

Council of Science Editors:

Ling EP. An Operator-Theoretic Approach for Traffic Prediction. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61167

.