Title :
Sequential Prediction of Unbounded Stationary Time Series
Author :
Györfi, László ; Ottucsák, György
Author_Institution :
Dept. of Comput. Sci. & Inf. Theor., Budapest Univ. of Technol. & Econ.
fDate :
5/1/2007 12:00:00 AM
Abstract :
A simple on-line procedure is considered for the prediction of a real-valued sequence. The algorithm is based on a combination of several simple predictors. If the sequence is a realization of an unbounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. An analog result is offered for the classification of binary processes
Keywords :
Bayes methods; binary sequences; pattern classification; prediction theory; random processes; time series; Bayes predictor; binary process; ergodic random process; real-valued sequential prediction; stationary time series; Automation; Convergence; Economic forecasting; High-speed networks; Informatics; Pattern recognition; Random processes; Random variables; Statistical learning; Telecommunication computing; On-line learning; pattern recognition; sequential prediction; time series; universal consistency;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2007.894660
