Book — 1 online resource (x, 229 pages) : illustrations (some color) Digital: text file.PDF.
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.
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.