@article{1b68d119dfda4a149fe68a62ca4217e7,

title = "Universal linear prediction by model order weighting",

abstract = "A common problem that arises in adaptive filtering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error for any bounded individual sequence is as good as the performance attainable by the best sequential linear predictor of order less than some M. This predictor is found by transforming linear prediction into a problem analogous to the sequential probability assignment problem from universal coding theory. The resulting universal predictor uses essentially a performance-weighted average of all predictors for model orders less than M. Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order. Examples of prediction performance are provided for autoregressive and speech data as well as an example of adaptive data equalization.",

author = "Singer, {Andrew C.} and Meir Feder",

note = "Funding Information: Manuscript received August 26, 1997; revised February 20, 1999. This work was prepared through collaborative participation in the Advanced Telecommunications/Information Distribution Research Program (ATIRP) Consortium and supported by the U.S. Army Research Laboratory under the Federated Laboratory Program, Cooperative Agreement DAAL01-96-2-0002. It was also supported in part by a grant from the Israeli Science Foundation. The associate editor coordinating the review of this paper and approving it for publication was Dr. Ali H. Sayed.",

year = "1999",

doi = "10.1109/78.790651",

language = "אנגלית",

volume = "47",

pages = "2685--2699",

journal = "IEEE Transactions on Signal Processing",

issn = "1053-587X",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "10",

}