Lemaire / Malinowski / Bagnall Advanced Analytics and Learning on Temporal Data

4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers
Erscheinungsjahr 2020
ISBN: 978-3-030-39098-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 11986, 229 Seiten, eBook

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Lemaire, Vincent

Malinowski, Simon

Bagnall, Anthony

Bondu, Alexis

Guyet, Thomas

Tavenard, Romain

Inhaltsverzeichnis

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.