Semi-supervised Time Series Classification Through Image Representations

Time series data is of crucial importance in different domains, such as financial and medical applications. However, obtaining a large amount of labeled time series data is an expensive and time-consuming task, which becomes the process of building an effective machine learning model a challenge. In these scenarios, algorithms that can deal with reduced amounts of labeled data emerge. One example is Semi-Supervised Learning (SSL), which has the capability of exploring both labeled and unlabeled data for tasks such as classification. In this work, a kNN graph-based transductive SSL approach is used for time series classification. A feature extraction step, based on imaging time series and obtaining features using deep neural networks is performed before the classification step. An extensive evaluation is conducted over four datasets, and a parametric analysis of the nearest neighbors is performed. Also, a statistical analysis over the obtained distances is conducted. Results suggest that our methods are suitable for classification and competitive with supervised baselines in some datasets.

Palavras-chave

Classification, Feature Extraction, Graph, Images, Neural Networks, Time Series, Transductive Semi Supervised Learning

Idioma

Inglês

Citação

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 13957 LNCS, p. 48-65.