Short-term time series algebraic forecasting with internal smoothing

Authors:
Rita Palivonaite;Minvydas Ragulskis
Affiliations:
-;-
Venue:
Neurocomputing
Year:
2014

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Abstract

A new algebraic forecasting method with internal smoothing is proposed for short-term time series prediction. The concept of the H-rank of a sequence is exploited for the detection of a base algebraic fragment of the time series. Evolutionary algorithms are exploited for the identification of the set of corrections which are used to perturb the original time series. The proposed forecasting method is constructed to find a near-optimal balance between the variability of algebraic predictors and the smoothness of averaging methods. Numerical experiments with an artificially generated and real-world time series are used to illustrate the potential of the proposed method.