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, Representation of ED between two Time Series, p.14

, Comparison of Manhattan Distance and Euclidean Distance, p.14

, Representation of DTW distance between two time series and its optimal warping path

, Representation of global constraints for Dynamic Time Warping

, Example of LCSS distance between two sequences, p.18

, Illustration of temporal distortion: Three time series per class (one class per line) for CBF & Gun_Point datasets, p.36

. .. , Illustration of temporal order importance: Two_Patterns dataset (Only one time series per class for readibility), p.36

, 45 2.2 SIFT -Match between the two images

. .. Botsw--scale-space-extrema-detection and . .. .-.-.-.-.-.-.-.-;-d-botsw, 49 2.5 Detection of extrema using Difference-of-Gaussians function, 7 Error rates of BoTSW, vol.50, p.57

, Error rates of D-BoTSW with and without normalization, p.57

, Per-dimension energy of D-BoTSW vectors extracted from dataset ShapesAll. The same codebook is used for all normalization schemes so that dimensions are comparable across all three sub-figures

. Ed-nn, . Dtw-nn, and L. ). Bop, Error rates for D-BoTSW (SSR+L 2 ) versus standalone baseline classifiers, p.59

S. Tsbf and . .. Boss), Error rates for D-BoTSW (SSR+L 2 ) versus standalone baseline classifiers, p.60

C. ). Prop, 62 2.13 Mean profiles for OliveOil and Wine datasets per class, Error rates for D-BoTSW (SSR+L 2 ) versus baseline ensemble classifiers, p.63

, Schematization of a Multi-Layer Perceptron with two hidden layers

, Convolutional Neural Network -Image Convolution, p.69

. .. Convolutional-neural-network--overview, 70 List of Figures

. .. Examples-of-adversarial-image, , p.71

, Learning time series shapelets as a convolution neural network 74

). .. , 79 3.10 Comparison of error rates obtained by LTS and ABS (with a perturbation in {0.001, 0.01, 0.1}), Function ?(z) = log(1 + exp(z)

. Ed-nn, . Dtw-nn, . Bop, and ). .. Lts-bagnall, Error rates for ABS versus standalone baseline classifiers, vol.87

S. Tsbf and . .. Boss), Error rates for ABS versus standalone baseline classifiers

, Error rates for ABS versus baseline ensemble classifiers (PROP and COTE)

. .. , Reunion island localization (from Wikipedia), p.98

. .. Ndvi), 100 4.4 Representation of five randomly selected time series per class from TiSeLaC dataset (NDVI)

, Map of the study area with field data location, p.104

. .. , Brazilian Amazon -Mean profiles per class (EVI), p.105

, Brazilian Amazon -Five randomly selected time series per class

, TiSeLaC -Evolution of error rates for an increasing number of training time series

, Brazilian Amazon -Evolution of error rates for an increasing number of training time series

, Brazilian Amazon -The 6 shapelets generated by ABS (of length 12)

, Brazilian Amazon -The 16 generated features from D-BoTSW algorithm

, Brazilian Amazon -Average histogram per class (D-BoTSW) 115

, Comparison of various similarity measures, p.23

D. .. Ed, The bold values indicate that the difference is significant, e.g. DTW-NN is significantly better than ED-NN since the p-value is equal to 0.001 < 5%. The last column provides the number of times a similarity measure is significantly better than another one, e.g. DTW is significantly better than 2 other similarity measures, Comparison of similarity measures (associated with 1-NN) using one-sided Wilcoxon Signed Rank Test p-values

. .. Comparison-of-time-series-classifiers, , p.34

. .. Uea-/-ucr-database-in-numbers, , p.37

D. , One sided Wilcoxon Test p-values, p.61

. .. , ABS -One sided Wilcoxon Test p-values, p.89

, Remote sensing time series datasets in numbers, p.107

, Average error rates on remote sensing time series datasets (on 6 runs)

, Weights associated with the shapelets represented on Figure 4

A. , Error rates from various time series classification methods from 50words to MALLAT

, Error rates from various time series classification methods from Meat to yoga

A. Bailly, Classification de séries temporelles avec applications en télédétection, 2018.

. .. Art, 9 1.1 Basic Definitions and Notations, Time Series Classification State-of-the, vol.13

. .. Classifiers, Feature-based Time Series

P. .. Aproximations, , vol.26

. .. Symbolic-fourier-approximation, 26 1.3.1.5 SIFT-based Representation, p.27

. .. Bag-of-patterns, , p.28

. .. Sax-vsm, 29 1.3.2.4 Bag-of-Words based on Discrete Wavelet Coefficients

. .. Bag-of-sfa-symbols, , p.29

L. .. Shapelets, , p.31

E. Classifiers-for-time and . .. Series, , p.33

, Collective Of Transformation-based Ensembles 33

, Summary on Time Series Classification Algorithms, vol.34

. .. , 37 1.6.1 Metrics for Evaluating Time Series Classifiers Performance 37

. Uea-/-ucr, Database, vol.37

, Other challenging problems related to Time Series Classification

. .. , 48 2.2.1 Keypoints extraction in time series 49 2.2.1.1 Scale-space extrema detection, vol.2

, 2.3 (Dense) Bag-of-Temporal-SIFT-Words for Time Series Classification 51 2.2.3.1 Bag-of-Words, p.55

, Comparison of Dense Extraction and Scale-Space Extrema Detection 56

, TiSeLaC Dataset versus Brazilian Amazon Dataset, p.107

, 107 of Robutness Methods for Remote Sensing Data Specificities: Growing Amount of Data 109

. Adversarially-built and . .. Shapelets, 2 Dense Bag-of-Temporal-SIFT-Words, p.113

.. .. Conclusion,

P. .. Conclusion,

.. .. Perspectives,

.. .. Bibliography,

.. .. Appendices,

. .. List-of-figures,

. .. List-of-tables,

.. .. Contents,

A. Bailly, Classification de séries temporelles avec applications en télédétection, 2018.