This paper considers the online trend estimation in the non-stationary stochastic optimization framework, where the comparator sequence satisfy certain variational constraints. The main contribution is a polynomial time policy extending Vovk-Azoury-Warmuth forecaster the achieves the minimax optimal rate for dynamic regret. All reviewers liked the paper, appreciating connecting the batch non-parametric regression to online stochastic optimization, techniques from wavelet computation, a model based on variational constraints which nicely captures sparsity and intensity of changes, and the (asymptotically) optimal algorithm.