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Intro; Preface; Organization; Contents; Time Series Representation and Compression; Symbolic Representation of Time Series: A Hierarchical Coclustering Formalization; 1 Introduction; 2 Related Work; 3 Formalization of the SAXO Approach; 3.1 Prior Distribution of the SAXO Models; 3.2 Likelihood of Data Given a SAXO Model; 3.3 Evaluation Criterion; 4 Comparative Experiments on Real Datasets; 4.1 Coding Length Evaluation; 4.2 Supervised Learning Evaluation; 5 Conclusion and Perspectives; References; Dense Bag-of-Temporal-SIFT-Words for Time Series Classification; 1 Introduction; 2 Related Work
2.1 Distance-Based Time Series Classification2.2 Bag-of-Words for Time Series Classification; 2.3 Ensemble Classifiers for Time Series; 3 Bag-of-Temporal-SIFT-Words (BoTSW); 3.1 Keypoint Extraction in Time Series; 3.2 Description of the Extracted Keypoints; 3.3 Bag-of-Temporal-SIFT-Words for Time Series Classification; 4 Experiments and Results; 4.1 Experimental Setup; 4.2 Dense Extraction vs. Scale-Space Extrema Detection; 4.3 Impact of the BoW Normalization; 4.4 Comparison with State-of-the-Art Methods; 5 Conclusion; References
Dimension Reduction in Dissimilarity Spaces for Time Series Classification1 Introduction; 2 Related Work; 3 Dissimilarity Representations of Time Series; 3.1 The Basic Idea; 3.2 Dynamic Time Warping Distance; 3.3 Dissimilarity Representations; 3.4 Learning Classifiers in Dissimilarity Space; 3.5 Prototype Dependent Kernels; 4 Experiments; 4.1 Data; 4.2 Classifiers; 4.3 Experimental Protocol; 4.4 Results; 5 Conclusion; A Performance Profiles; References; Time Series Classification and Clustering; Fuzzy Clustering of Series Using Quantile Autocovariances; 1 Introduction
2 A Dissimilarity Based on Quantile Autocovariances3 Fuzzy Clustering Based on Quantile Autocovariances; 4 Simulation Study; 5 A Case Study; 6 Concluding Remarks; References; A Reservoir Computing Approach for Balance Assessment; 1 Introduction; 2 Balance Assessment Using Reservoir Computing; 3 Experimental Results; 4 Conclusions; References; Learning Structures in Earth Observation Data with Gaussian Processes; 1 Introduction; 2 Gaussian Process Regression; 2.1 Gaussian Processes: A Gentle Introduction; 2.2 On the Model Selection; 2.3 On the Covariance Function
2.4 Gaussian Processes Exemplified3 Advances in Gaussian Process Regression; 3.1 Structured, Non-stationary and Multiscale; 3.2 Time-based Covariance for GPR; 3.3 Heteroscedastic GPR: Learning the Noise Model; 3.4 Warped GPR: Learning the Output Transformation; 3.5 Source Code and Toolboxes; 4 Analysis of Gaussian Process Models; 4.1 Ranking Features Through the ARD Covariance; 4.2 Uncertainty Intervals; 5 Conclusions and Further Work; References; Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes; 1 Introduction; 2 Methods Used
2.1 Distance-Based Time Series Classification2.2 Bag-of-Words for Time Series Classification; 2.3 Ensemble Classifiers for Time Series; 3 Bag-of-Temporal-SIFT-Words (BoTSW); 3.1 Keypoint Extraction in Time Series; 3.2 Description of the Extracted Keypoints; 3.3 Bag-of-Temporal-SIFT-Words for Time Series Classification; 4 Experiments and Results; 4.1 Experimental Setup; 4.2 Dense Extraction vs. Scale-Space Extrema Detection; 4.3 Impact of the BoW Normalization; 4.4 Comparison with State-of-the-Art Methods; 5 Conclusion; References
Dimension Reduction in Dissimilarity Spaces for Time Series Classification1 Introduction; 2 Related Work; 3 Dissimilarity Representations of Time Series; 3.1 The Basic Idea; 3.2 Dynamic Time Warping Distance; 3.3 Dissimilarity Representations; 3.4 Learning Classifiers in Dissimilarity Space; 3.5 Prototype Dependent Kernels; 4 Experiments; 4.1 Data; 4.2 Classifiers; 4.3 Experimental Protocol; 4.4 Results; 5 Conclusion; A Performance Profiles; References; Time Series Classification and Clustering; Fuzzy Clustering of Series Using Quantile Autocovariances; 1 Introduction
2 A Dissimilarity Based on Quantile Autocovariances3 Fuzzy Clustering Based on Quantile Autocovariances; 4 Simulation Study; 5 A Case Study; 6 Concluding Remarks; References; A Reservoir Computing Approach for Balance Assessment; 1 Introduction; 2 Balance Assessment Using Reservoir Computing; 3 Experimental Results; 4 Conclusions; References; Learning Structures in Earth Observation Data with Gaussian Processes; 1 Introduction; 2 Gaussian Process Regression; 2.1 Gaussian Processes: A Gentle Introduction; 2.2 On the Model Selection; 2.3 On the Covariance Function
2.4 Gaussian Processes Exemplified3 Advances in Gaussian Process Regression; 3.1 Structured, Non-stationary and Multiscale; 3.2 Time-based Covariance for GPR; 3.3 Heteroscedastic GPR: Learning the Noise Model; 3.4 Warped GPR: Learning the Output Transformation; 3.5 Source Code and Toolboxes; 4 Analysis of Gaussian Process Models; 4.1 Ranking Features Through the ARD Covariance; 4.2 Uncertainty Intervals; 5 Conclusions and Further Work; References; Monitoring Short Term Changes of Infectious Diseases in Uganda with Gaussian Processes; 1 Introduction; 2 Methods Used