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