• Data-driven detrending of nonstationary fractal time series with echo state networks 

      Maiorino, Enrico; Bianchi, Filippo Maria; Livi, Lorenzo; Rizzi, Antonello; Sadeghian, Alireza (Journal article; Tidsskriftartikkel, 2016-12-14)
      In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evolves through time. We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo ...
    • Temporal overdrive recurrent neural network 

      Bianchi, Filippo Maria; Kampffmeyer, Michael C.; Maiorino, Enrico; Jenssen, Robert (Chapter; Bokkapittel, 2017-07-03)
      In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics. The proposed network is composed by several recurrent groups of neurons that are trained to separately adapt to each timescale, in order to improve the system identification process. We test our framework on time series prediction tasks ...