Advanced Analytics and Learning on Temporal Data
4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers
(Sprache: Englisch)
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...
The 7 full papers presented together with 9 poster papers were...
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Klappentext zu „Advanced Analytics and Learning on Temporal Data “
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.
Inhaltsverzeichnis zu „Advanced Analytics and Learning on Temporal Data “
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.Bibliographische Angaben
- 2020, 1st ed. 2020, X, 229 Seiten, 90 farbige Abbildungen, Maße: 15,6 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Vincent Lemaire, Simon Malinowski, Anthony Bagnall, Alexis Bondu, Thomas Guyet, Romain Tavenard
- Verlag: Springer, Berlin
- ISBN-10: 3030390977
- ISBN-13: 9783030390976
Sprache:
Englisch
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