Enhanced Evolutionary Sequential Minimal Optimization Model for Inflation Prediction

  • Abstract
  • Keywords
  • References
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  • Abstract

    The control of inflation rate is at the core of monetary policy making. Therefore, there is very great interest in reliable inflation forecasts by central bankers to help them achieve this aim. The aim of this investigation has been to forecast inflation in case of the United States as accurately as possible. This paper proposes a new forecasting model called Sequential Minimal Organization (SMOreg-3passes) for regression predictions. SMOreg-3passes consists of four steps, they are technical indicators generation, feature selection, normalization regression and regression forecaster. The proposed model evaluated using two regression measurements (Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)). Our evidence from the SMOreg-3passes model suggests that the chronology of time series has great influence on future forecasting and the error in forecasting the past has an exponential impact on the current data. The results showed that the proposed model outperformed the traditional SMO and Multiple Layer Perception (MLP) methods.


  • Keywords

    Inflation Rate, Technical Indicators, Feature Selection, Sequential Minimal Optimization (SMO).

  • References

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Article ID: 27998
DOI: 10.14419/ijet.v7i4.19.27998

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