Vanlı, N. DenizcanSayın, Muhammed O.Ergüt, S.Kozat, Süleyman S.2016-02-082016-02-082014-092219-5491http://hdl.handle.net/11693/27868Date of Conference: 1-5 Sept. 2014Conference name: 22nd European Signal Processing Conference (EUSIPCO) 2014We 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.EnglishLower boundSequential predictionWorst-case performanceForecastingSequential switchingSignal processingParameter estimation problemsRelative performanceSequential algorithmSequential predictionSquared error loss functionsStructural assumptionParameter estimationConference Paper