  Fourier methods have a long and proven track record as an excellent tool in
data processing. As memory and computational constraints gain importance in
embedded and mobile applications, we propose to combine Fourier methods and
recurrent neural network architectures. The short-time Fourier transform allows
us to efficiently process multiple samples at a time. Additionally, weight
reductions trough low pass filtering is possible. We predict time series data
drawn from the chaotic Mackey-Glass differential equation and real-world power
load and motion capture data.
