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We study how we can adapt a predictor to a non-stationary environment withadvises from multiple experts. We study the problem under complete feedbackwhen the best expert changes over time from a decision theoretic point of view.Proposed algorithm is based on popular exponential weighing method withexponential discounting. We provide theoretical results bounding regret underthe exponential discounting setting. Upper bound on regret is derived forfinite time horizon problem. Numerical verification of different real lifedatasets are provided to show the utility of proposed algorithm.

An aggregating strategy for shifting experts in discrete sequence prediction