Title :
Limits to consistent on-line forecasting for ergodic time series
Author :
Györfi, Laszlo ; Morvai, Gusztav ; Yakowitz, Sidney J.
Author_Institution :
Dept. of Math. & Comput. Sci., Tech. Univ. Budapest, Hungary
fDate :
3/1/1998 12:00:00 AM
Abstract :
This article concerns problems of time-series forecasting under the weakest of assumptions. Related results are surveyed and are points of departure for the developments here, some of which are new and others are new derivations of previous findings. The contributions in this study are all negative, showing that various plausible prediction problems are unsolvable, or in other cases, are not solvable by predictors which are known to be consistent when mixing conditions hold
Keywords :
nonparametric statistics; parameter estimation; prediction theory; random processes; time series; consistent on-line forecasting limits; dynamic forecasting; ergodic time series; mixing conditions; nonparametric estimation; prediction problems; random variable sequence; Books; Extrapolation; Gaussian processes; Interpolation; Kernel; Least squares approximation; Prediction theory; Random variables; Recursive estimation; Smoothing methods;
Journal_Title :
Information Theory, IEEE Transactions on
