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Advanced Analytics and Learning on Temporal Data

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Cover of 'Advanced Analytics and Learning on Temporal Data'

Table of Contents

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    Book Overview
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    Chapter 1 Robust Functional Regression for Outlier Detection
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    Chapter 2 Transform Learning Based Function Approximation for Regression and Forecasting
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    Chapter 3 Proactive Fiber Break Detection Based on Quaternion Time Series and Automatic Variable Selection from Relational Data
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    Chapter 4 A Fully Automated Periodicity Detection in Time Series
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    Chapter 5 Conditional Forecasting of Water Level Time Series with RNNs
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    Chapter 6 Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories
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    Chapter 7 Localized Random Shapelets
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    Chapter 8 Feature-Based Gait Pattern Classification for a Robotic Walking Frame
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    Chapter 9 How to Detect Novelty in Textual Data Streams? A Comparative Study of Existing Methods
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    Chapter 10 Seq2VAR: Multivariate Time Series Representation with Relational Neural Networks and Linear Autoregressive Model
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    Chapter 11 Modelling Patient Sequences for Rare Disease Detection with Semi-supervised Generative Adversarial Nets
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    Chapter 12 Extended Kalman Filter for Large Scale Vessels Trajectory Tracking in Distributed Stream Processing Systems
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    Chapter 13 Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning
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    Chapter 14 Learning Stochastic Dynamical Systems via Bridge Sampling
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    Chapter 15 Quantifying Quality of Actions Using Wearable Sensor
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    Chapter 16 An Initial Study on Adapting DTW at Individual Query for Electrocardiogram Analysis
Attention for Chapter 7: Localized Random Shapelets
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Chapter title
Localized Random Shapelets
Chapter number 7
Book title
Advanced Analytics and Learning on Temporal Data
Published by
Springer, Cham, September 2019
DOI 10.1007/978-3-030-39098-3_7
Book ISBNs
978-3-03-039097-6, 978-3-03-039098-3
Authors

Mael Guillemé, Simon Malinowski, Romain Tavenard, Xavier Renard

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