Abstract

In prediction problems of communication and control theory, it has become increasingly obvious that there are many applications in which a priori assumptions regarding data statistics are not justified. Thus, systems must be designed to take maximum advantage of whatever statistics are encountered. Unfortunately, these systems are inherently nonlinear in operation, which makes it difficult, ff not impossible, to evaluate their performance. In this paper the asymptotic form of the mean square prediction error is found for a stationary Gaussian time series when the prediction is a linear weighting of the immediate past, the weights being "learned" from the data. Computer results are given to demonstrate the usefulness of the asymptotic formula.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.