Stability of Time Series Models Based on Fractional-Order Weakening Buffer Operators

Abstract

Different weakening buffer operators in a time-series model analysis usually result in different model sensitivities, which sometimes affect the effectiveness of relevant operator-based methods. In this paper, the stability of two classic fractional-order weakening buffer operator-based series models is studied; then, a new data preprocessing method based on a novel fractional-order bidirectional weakening buffer operator is provided, whose effect in improving the model’s stability is tested and utilized in prediction problems. Practical examples are employed to demonstrate the efficiency of the proposed method in improving the model’s stability in noise scenarios. The comparison indicates that the proposed method overcomes the disadvantage of many weakening buffer operators in the subjectively biased weighting of the new or old information in forecasting. These expand the application of the proposed method in time series analysis.