Facilitating time series classification by linear law-based feature space transformation

Abstract

The aim of this paper is to perform uni- and multivariate time series classification tasks with linear law-based feature space transformation (LLT). First, LLT is used to separate the training and test sets of instances. Then, it identifies the governing patterns (laws) of each input sequence in the training set by applying time-delay embedding and spectral decomposition. Finally, it uses the laws of the training set to transform the feature space of the test set. These calculation steps have a low computational cost and the potential to form a learning algorithm. For the empirical study of LLT, a widely used human activity recognition database called AReM is employed. Based on the results, LLT vastly increases the accuracy of traditional classifiers, outperforming state-of-the-art methods after the proposed feature space transformation is applied. The fastest error-free classification on the test set is achieved by combining LLT and the k-nearest neighbor (KNN) algorithm while performing fivefold cross-validation.