DyReg-FResNet: Unsupervised Feature Space Amplified Dynamic Regularized Residual Network for Time Series Classification

Time Series Classification (TSC) is a challenging problem owing to the practical constraints of lack of availability of training examples and insufficient sample points in the training instances. In order to ensure the construction of a robust trained model (under practical constraints) to address TSC, we propose DyReg-FResNet, which is a dynamically regularized Residual Network (ResNet), amplified by unsupervised feature space training. We generate signal processing, information theoretic and statistical features to augment the representation learning of the ResNet. The unsupervised features are capable of extracting the morphological and structural characteristics of the time series signals, whereas our proposed dynamic regularizer trades off by reducing substantial variance while not perturbing the bias. The regularization factor is a function of the signal dynamics (in effect, regularization factor is different for different training sets). DyReg-FResNet learns through residual mapping to minimize the exploding or vanishing gradient problems, amplified unsupervised features amplify the representation space by introducing lower level representation to guide the learning towards the basins of attraction of minima and dynamic regularizer minimizes the generalization error. We extensively experiment with publicly available UCR time series datasets. DyReg-FResNet demonstrates extremely superior performance by consistently outperforming the existing benchmark results as well as current state-of-the-art algorithms.