Advanced Analysis and Learning on Temporal Data

Name: Advanced Analysis and Learning on Temporal Data | First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers
Brand: Springer
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First ECML PKDD Workshop, AALTD 2015, Porto, Portugal, September 11, 2015, Revised Selected Papers

Ahlame Douzal-Chouakria José A. Vilar Pierre-François Marteau(Herausgeber*in)

Springer (Verlag)

Erschienen am 3. August 2016

X, 173 Seiten

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978-3-319-44412-3 (ISBN)

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Inhalt

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 Classification
2.2 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 Classification
1 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 Autocovariances
3 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 Exemplified
3 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 Coregionalized Gaussian Processes
2.2 Signal Decomposition with GP
3 Application to HMIS Data
3.1 Coregionalization of Health Facilities Data
3.2 Aggregate Missing Data Estimation
3.3 Monitoring Short Term Changes with GP
4 Application to Simulated Data
5 Final Discussion
References
Estimating Dynamic Graphical Models from Multivariate Time-Series Data: Recent Methods and Results
1 Introduction
2 Gaussian Graphical Models
3 Model Selection
3.1 An Estimation Framework
3.2 Maximum Likelihood Estimation
3.3 Sparsity Assumptions
3.4 Theoretical Results for Sparse Precision Estimation
4 Dynamic Graph Models
4.1 Continuous Evolution
4.2 Edge-Piecewise Dynamics
4.3 Graph-Piecewise Dynamics
5 Dynamic GGM Estimation
5.1 Local Kernel/Window Estimation
5.2 Regularised Temporal Variation
6 Example Application
7 Conclusion
References
Metric Learning for Time Series Comparison
A Multi-modal Metric Learning Framework for Time Series kNN Classification
1 Introduction
2 Time Series Metrics
3 Temporal and Frequential Metric Learning for a Large Margin kNN
4 Experiments
5 Conclusion
References
A Comparison of Progressive and Iterative Centroid Estimation Approaches Under Time Warp
1 Introduction
2 Progressive and Iterative Approaches
2.1 Progressive Approaches
2.2 Iterative Approaches
3 Experimental Study
3.1 Data Description
3.2 Validation Process
3.3 Discussion
4 Conclusion
References
Coarse-DTW for Sparse Time Series Alignment
1 Introduction
2 Previous Work
3 Presentation of Coarse-DTW
3.1 Classical DTW
3.2 Sparse Time Series
3.3 Coarse-DTW
4 Downsampling
5 Optimizations on Coarse-DTW
6 Kernelization of CoarseDTW
6.1 Normalization of KCoarse-DTW
7 Results
7.1 DTW vs. Coarse-DTW in 1-NN Classification
7.2 SVM Classification with KCoarse-DTW
8 Conclusions
References
Author Index

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