Transfer Learning of Shapelets for Time Series Classification Using Convolutional Neural Network

Abstract

Time series classification has a wide variety of possible applications, like outlines of figure types, signs of movement and sensor signals. This diversity may present different results with the application of machine learning techniques. Among the different ways to classify time series, two, in particular, are explored in this paper: the shapelet primitive and the neural network classification. A shapelet is a sub-sequence of the time series symbolic, like a high-level descriptor, that are representative for the class to which they belong. These features act as common knowledge used by domain experts. This paper proposes a CNN training protocol to achieve better results in the classification of time series. The idea consists of decomposing the original time series into shapelets and noise. Then, the shapelets are used to train a classifier while the no-shapelets (“signal noise”) is used to train another classifier. The original time series is then used to train two final classifiers starting with the weights of shapelet and no-shapelet classifiers. This previous extraction of this representation can improve classification ability in a convolutional neural network using transfer learning. The experimental evaluation shows that the pre-selection of shapelets before the network training changes the classifier results for several databases and, consequently, improves classification accuracy.