Any stationary time-series can be decomposed by means of an optimization operator, called the ζ-optimator, into several components (the time-series){Yti}, i =1,2,…, p, such that the first component {Vti} t = 1,2,…,v is a smooth process having a larger autocorrelation in comparison with the original process {Yt}, i.e. ρvi> ρy. Usually only a few such components are sufficient for approximating the time-series with good accuracy. The ζ-optimator involves a shape parameter a, so the decomposition is unique provided that a. is fixed. Since the component {Vt1} involves much of the useful information it can be used for computing predictors for control purposes. Thus, given the observations Yv, Yv-1, Yv-2,…, a predictor of Yv+1is ρviVv1(q) where, Vv1(q) = qYv+ q(1-q)2Yv-2, …, the weights q(1-q)r, r=0,1,2,…, decreasing rapidly as q = q(α) ϵ (0,1) Further, one may chooseqrather than choosing α, sinceq(α)is a one-one mapping. Onceqis fixed, the predictor ρv1Vv1(q) is obtained in a straightforward way by using the formula above. It is shown that ρv1Vv1(q) converges to the best predictor as α → 0. Some examples are worked out, illustrating both the decomposition and the forecasting procedures.