Adaptive Online Prediction Using Weighted Windows
Shin-ichi YOSHIDA, Kohei Hatano, Eiji Takimoto, Masayuki Takeda · IEICE Transactions on Information and Systems · 2011
We propose online prediction algorithms for data streams whose characteristics might change over time. Our algorithms are applications of online learning with experts. In particular, our algorithms combine base predictors over sliding windows with different length as experts. As a result, our algorithms are guaranteed to be competitive with the base predictor with the best fixed-length sliding window in hindsight.