Advanced Analytics and Learning on Temporal Data : 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers / edited by Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Alexis Bondu, Thomas Guyet, Romain Tavenard.

Format:

Book

Contributor:

Lemaire, Vincent (Computer scientist), editor.

Malinowski, Simon, editor.

Bagnall, Anthony, editor.

Bondu, Alexis, editor.

Guyet, Thomas, editor.

Tavenard, Romain, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 11986.

Lecture Notes in Artificial Intelligence ; 11986

Language:

English

Subjects (All):

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.

Local Subjects:

Artificial Intelligence.

Information Systems and Communication Service.

Computer Systems Organization and Communication Networks.

Computer Applications.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (X, 229 pages) : 109 illustrations, 90 illustrations in color.

Edition:

First edition 2020.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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. .

Contents:

Robust Functional Regression for Outlier Detection

Transform Learning Based Function Approximation for Regression and Forecasting

Proactive Fiber Break Detection based on Quaternion Time Series and Automatic Variable Selection from Relational Data

A fully automated periodicity detection in time series

Conditional Forecasting of Water Level Time Series with RNNs

Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories

Localized Random Shapelets

Feature-Based Gait Pattern Classification for a Robotic Walking Frame

How to detect novelty in textual data streams? A comparative study of existing methods

Seq2VAR: multivariate time series representation with relational neural networks and linear autoregressive model

Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets

Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems

Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets using Deep Learning

Learning Stochastic Dynamical Systems via Bridge Sampling

Quantifying Quality of Actions Using Wearable Sensor

An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis.

Other Format:

Printed edition:

ISBN:

978-3-030-39098-3

9783030390983

Access Restriction:

Restricted for use by site license.