Fourier RNNs for Sequence Prediction

Fourier methods have a long and proven track record as an excellent tool in data processing. We propose to integrate Fourier methods into complex recurrent neural network architectures and show accuracy improvements on prediction tasks as well as computational load reductions. We predict synthetic data drawn from synthetic-equations as well as real world power load data.