Recurrent neural networks for time series classification

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Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function (2012, background): “In the past two decades, recurrent neural networks have received considerable studies in many scientific and engineering fields, such as signal processing [9], pattern classification [10,11], control of chaos [12], optimization [13,14], etc.”
Wave physics as an analog recurrent neural network (2019, background): “[11] Michael Hsken and Peter Stagge, “Recurrent neural networks for time series classification,” Neurocomputing 50, 223–235 (2003).”
Weighted dynamic time warping for time series classification (2011, methodology): “Husken and Stagge [6] utilized recurrent neural networks for time series classification and Guler and Ubeyli [4] presented the wavelet-based adaptive neuro-fuzzy inference system model for classification of ectroencephalogram (EEG) signals.”
A review of unsupervised feature learning and deep learning for time-series modeling (2014, methodology): “RNNs can be seen as very deep networks with shared 197 parameters at each layer when unfolded in time.”