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Forecasting Time Series Using EMD-HW Bagging

Ahmad M. Awajan, Mohd Tahir Ismail, S. AL Wadi


In this study, we present a new technique for the bootstrap aggregation (bagging) for financial time series, which results in significant improvements in the forecasts. This technique is based on empirical mode decompositions (EMD), quantile regression (QR) and Holt-Winter model (HW). The bagging uses an EMD with QR to separate the time series into regression line, Intrinsic Mode Functions (IMFs) and residual. The IMFs are clustering into
two clusters, which are the High frequency and Low frequency. Then the High frequency is bootstrapped using a moving block bootstrap. A new series is assembled using this bootstrap. An ensemble of a hybrid EMD with HW model is estimated on the bootstrapped series. And the resulting point forecasts are combined. We evaluate this method on the daily stock market data of 10 countries. Based on the Root Mean Squared Error (RMSE),
Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) the results indicate that it outperforms the six traditional forecasting models and a hybrid EMD-HW without bagging consistently.


bootstrap aggregation (bagging), forecast time series, empirical mode decomposition, Holt-Winter forecasting method, intrinsic mode function, bootstrap, forecast accuracy measures.

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