Forecasting Time Series Using EMD-HW Bagging
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.
Disclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.