DocumentCode :
907042
Title :
The prediction error of stationary Gaussian time series of unknown covariance
Author :
Davisson, L.D.
Volume :
11
Issue :
4
fYear :
1965
fDate :
10/1/1965 12:00:00 AM
Firstpage :
527
Lastpage :
532
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.
Keywords :
Gaussian processes; Prediction methods; Time series; Computer errors; Control theory; Data compression; Error analysis; Error correction; Filling; Mean square error methods; Signal design; Statistics; Telemetry;
fLanguage :
English
Journal_Title :
Information Theory, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9448
Type :
jour
DOI :
10.1109/TIT.1965.1053829
Filename :
1053829
Link To Document :
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