Short-term time series algebraic forecasting with mixed smoothing

Short-term time series algebraic prediction technique with mixed smoothing is presented in this paper. Evolutionary algorithms are employed for the identification of a near-optimal algebraic skeleton from the available data. Direct algebraic predictions are conciliated by internal errors of interpolation and external differences from the moving average. Computational experiments with real world time series are used to demonstrate the effectiveness of the proposed forecasting algorithm.