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Advanced Analytics and Learning on Temporal Data : 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers
Authors: --- --- --- --- --- et al.
ISBN: 3030390985 3030390977 Year: 2020 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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Abstract

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data. .

Keywords

Artificial intelligence. --- Computers. --- Computer organization. --- Application software. --- Optical data processing. --- Artificial Intelligence. --- Information Systems and Communication Service. --- Computer Systems Organization and Communication Networks. --- Computer Applications. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Organization, Computer --- Electronic digital computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- AI (Artificial intelligence) --- Artificial thinking --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Optical equipment --- Time-series analysis --- Machine learning --- Temporal databases --- Data processing --- Temporal data bases --- Databases --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities


Book
Advanced Analytics and Learning on Temporal Data
Authors: --- --- --- --- --- et al.
ISBN: 9783030390983 Year: 2020 Publisher: Cham Springer International Publishing :Imprint: Springer


Multi
Advanced Analytics and Learning on Temporal Data : 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers
Authors: --- --- --- --- --- et al.
ISBN: 9783030390983 Year: 2020 Publisher: Cham Springer International Publishing

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Export citation

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Bookmark

Abstract

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Würzburg, Germany, in September 2019. The 7 full papers presented together with 9 poster papers were carefully reviewed and selected from 31 submissions. The papers cover topics such as temporal data clustering; classification of univariate and multivariate time series; early classification of temporal data; deep learning and learning representations for temporal data; modeling temporal dependencies; advanced forecasting and prediction models; space-temporal statistical analysis; functional data analysis methods; temporal data streams; interpretable time-series analysis methods; dimensionality reduction, sparsity, algorithmic complexity and big data challenge; and bio-informatics, medical, energy consumption, on temporal data. .

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