  In this paper it is reconsidered the prediction problem in time series
framework by using a new non-parametric approach. Through this reconsideration,
the prediction is obtained by a weighted sum of past observed data. These
weights are obtained by solving a constrained linear optimization problem that
minimizes an outer bound of the prediction error. The innovation is to consider
both deterministic and stochastic assumptions in order to obtain the upper
bound of the prediction error, a tuning parameter is used to balance these
deterministic-stochastic assumptions in order to improve the predictor
performance. A benchmark is included to illustrate that the proposed predictor
can obtain suitable results in a prediction scheme, and can be an interesting
alternative method to the classical non-parametric methods. Besides, it is
shown how this model can outperform the preexisting ones in a short term
forecast.
