Comprehensive lower bounds on sequential prediction
European Signal Processing Conference
European Signal Processing Conference, EUSIPCO
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/27868
We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as regret, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case. © 2014 EURASIP.
- Conference Paper 
Showing items related by title, author, creator and subject.
Vanli, N.D.; Kozat, S.S. (Institute of Electrical and Electronics Engineers Inc., 2015)We study sequential prediction of real-valued, arbitrary, and unknown sequences under the squared error loss as well as the best parametric predictor out of a large, continuous class of predictors. Inspired by recent results ...
Vanli N.D.; Gokcesu K.; Sayin M.O.; Yildiz H.; Kozat S.S. (Institute of Electrical and Electronics Engineers Inc., 2016)We study sequential compound decision problems in the context of sequential prediction of real valued sequences. In particular, we consider finite state (FS) predictors that are constructed based on a hierarchical structure, ...
Arslan Ö.; Saranli, U. (2012)An important motivation for work on legged robots has always been their potential for high-performance locomotion on rough terrain. Nevertheless, most existing control algorithms for such robots either make rigid assumptions ...