Estimating time series future optima using a steepest descent methodology as a backtracker

Recently it was produced a backtrack technique for the efficient approximation of a time seriespsila future optima. Such an estimation is succeeded based on a selection of sequenced points produced from the repetitive process of the continuous optima finding. Additionally, it is shown that if any time series is treated as an objective function subject to the factors affecting its future values, the use of any optimization technique finally points local optimum and therefore enables accurate prediction making. In this paper the backtrack technique is compiled with a steepest descent methodology towards optimization.