Aykut Koc Lab has made a contribution at the intersection of classical signal processing and machine learning by introducing the fractional Fourier transform (FrFT) to the machine learning domain for time series analysis in the paper “Fractional Fourier Transform in Time Series Prediction“. Our architecture utilizing FrFT with Recurrent Neural Networks (RNNs) for time series prediction problem is now out at IEEE Signal Processing Letters! We show that FrFT-based RNN models give better prediction performances than those based on either time and frequency domain by utilizing the parametric nature of FrFT that provides us to reach and select from infinitely many signal domains to represent time-series signals for better prediction.