We present a general approach for obtaining stochastic regret bounds for non-convex loss functions holding with high probability. Scale-free algorithms can solve Stochastic Online Convex Optimization using "surrogate losses" regret analysis. Then, we provide optimal prediction and probabilistic forecasting methods for non-stationary unbounded time series.