Apprentissage profond pour la classification des séries temporelles
Hassan Ismail Fawaz · theses.fr (ABES) · 2020
Data science is about designing algorithms and pipelines for extracting knowledge from large masses of data.Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time.Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over time.Their analysis can reveal trends, relationships and similarities across the data.There exists numerous fields containing data in the form of time series: health care (electrocardiogram, blood sugar, etc.), activity recognition, remote sensing, finance (stock market price), industry (sensors), etc.In data mining, classification is a supervised task that involves learning a model from labeled data organized into classes in order to predict the correct label of a new instance.Time series classification consists of constructing algorithms dedicated to automatically label time series data.For example, using a labeled set of electrocardiograms from healthy patients or patients with a heart disease, the goal is to train a model capable of predicting whether or not a new electrocardiogram contains a pathology.The sequential aspect of time series data requires the development of algorithms that are able to harness this temporal property, thus making the existing off-the-shelf machine learning models for traditional tabular data suboptimal for solving the underlying task.In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision.The main objective of this thesis was to study and develop deep neural networks specifically constructed for the classification of time series data.We thus carried out the first large scale experimental study allowing us to compare the existing deep methods and to position them compared other non-deep learning based state-of-the-art methods.Subsequently, we made numerous contributions in this area, notably in the context of transfer learning, data augmentation, ensembling and adversarial attacks.Finally, we have also proposed a novel architecture, based on the famous Inception network (Google), which ranks among the most efficient to date.Our experiments carried out on benchmarks comprising more than a hundred data sets enabled us to validate the performance of our contributions.Finally, we also showed the relevance of deep learning approaches in the field of surgical data science where we proposed an interpretable approach in order to assess surgical skills from kinematic multivariate time series data.