1.

Record Nr.

UNISA996418295403316

Titolo

Advanced analytics and learning on temporal data : 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, revised selected papers / / Vincent Lemaire [and five others] (editors)

Pubbl/distr/stampa

Cham, Switzerland : , : Springer, , [2021]

©2021

ISBN

3-030-65742-6

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (X, 233 p. 88 illus., 67 illus. in color.)

Collana

Lecture Notes in Artificial Intelligence ; ; 12588

Disciplina

610.730692

Soggetti

Nurse practitioners

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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 -- Bio-Informatics, Medical, Energy Consumption, Temporal Data.

Sommario/riassunto

This book constitutes the refereed proceedings of the 4th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2019, held in Ghent, Belgium, in September 2020. The 15 full papers presented in this book were carefully reviewed and selected from 29 submissions. The selected papers are devoted to 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,



Temporal Data.