Long-term prediction intervals of time series
IEEE Transactions on Information Theory
Sparsity regret bounds for individual sequences in online linear regression
The Journal of Machine Learning Research
| Hi-index | 754.90 |
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