TY - GEN
T1 - Twice universal linear prediction of individual sequences
AU - Singer, A. C.
AU - Feder, M.
PY - 1998
Y1 - 1998
N2 - We present a «twice universal» linear prediction algorithm over the unknown parameters and model orders, in which the sequentially accumulated square prediction error is as good as any linear predictor of order up to some M, for any individual sequence. The extra loss comprises of a parameter «redundancy» term proportional to (p/2)n-1/ln(n), and a model order «redundancy» term proportional to n-1/ln(p), where p, is the model order we compare with, and n is the data length. The computational complexity of the algorithm is about the complexity of a recursive least squares (RLS) linear predictor of order M.
AB - We present a «twice universal» linear prediction algorithm over the unknown parameters and model orders, in which the sequentially accumulated square prediction error is as good as any linear predictor of order up to some M, for any individual sequence. The extra loss comprises of a parameter «redundancy» term proportional to (p/2)n-1/ln(n), and a model order «redundancy» term proportional to n-1/ln(p), where p, is the model order we compare with, and n is the data length. The computational complexity of the algorithm is about the complexity of a recursive least squares (RLS) linear predictor of order M.
UR - http://www.scopus.com/inward/record.url?scp=84890355584&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890355584&partnerID=8YFLogxK
U2 - 10.1109/ISIT.1998.708726
DO - 10.1109/ISIT.1998.708726
M3 - Conference contribution
AN - SCOPUS:84890355584
SN - 0780350006
SN - 9780780350007
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 135
BT - Proceedings - 1998 IEEE International Symposium on Information Theory, ISIT 1998
T2 - 1998 IEEE International Symposium on Information Theory, ISIT 1998
Y2 - 16 August 1998 through 21 August 1998
ER -