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The predictive analysis of a water demand time series is a complex and non-linear problem that cannot, simply, be solved using techniques from algebra, calculus, differential equations, and partial differential equations. Because of its intimate relationship with computers and computer architecture, a numerical method is proposed as a possible solution. The iteration direction, incremental or decremental, of the proposed technique is determined by the gradient between the first and the last element of the model's input vector. In order to impose a fast rate of convergence to a solution, the iteration step size is biased by means of an offset. The results obtained demonstrate consistency on two different data sets, each containing a sum of 200 instances of data. Prediction accuracy values of 96.04% and 95.61% are obtained on the respective data sets, with rate of convergence values of 0.90 × 106 i.s-1 and 0.47 × 106 i.s-1.