Piet Delaney wrote:
circular convolution is used with the Fast Fourier Transform.
The frequency data goes from -N/2 ...0 ,,,, +N/2,
multiplying in the frequency domain is the same as
convolving in the time or space domain. The result of multiplying
a time series by say a filter is the same as convolving it
with the FFT of the filter. Both domains wrap around with the
FFT, so the normal convolution associated with the Fourier
transform is replace with the circular convolution.
Many prediction algorithms are based on digital signal processing.
The Kalman filter for example was used by Harvey for forecasting
financial markets. The kernel likely has lots of time series that
could be used for system identification for predicting how to best
use system resources.
Ok, but what is "circular convolution scheduling"?