Time Series Classification Using Time Warping Invariant Echo State Networks

Abstract

For many years, neural networks have gained gigantic interest and their popularity is likely to continue because of the success stories of deep learning. Nonetheless, their applications are mostly limited to static and not temporal patterns. In this paper, we apply time warping invariant Echo State Networks (ESNs) to time-series classification tasks using datasets from various studies in the UCR archive. We also investigate the influence of ESN architecture and spectral radius of the network in view of general characteristics of data, such as dataset type, number of classes, and amount of training data. We evaluate our results comparing it to other state-of-the-art methods, using One Nearest Neighbor (1-NN) with Euclidean Distance (ED), Dynamic Time Warping (DTW) and best warping window DTW.