Near-ideal causal smoothing filters for the real sequences
We suggest a family of causal smoothing filters for discrete time processes. These filters are near-ideal meaning that a higher rate of energy damping would lead to the loss of causality. These filters approximate non-causal filters transferring non-predicable processes into predictable ones. A possible application is preliminary smoothing of the inputs for predicting algorithms. Certain mild but stable improvement of forecasting accuracy is demonstrated in experiments with autoregressions.
smoothing filters predicting casual filters near-ideal filters lti filters