Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td align="left"><i>n</i>.head = 10</td><td align="center">RMSE</td><td align="center">MAE</td><td align="center">MASE</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td align="left"><span style="width: 108.7375px;"><svg height="17.5375" id="M64" style="vertical-align:-4.68874pt" version="1.1" viewbox="0 0 108.7375 17.5375" width="108.7375" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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</svg></span></td><td align="center">822.3858</td><td align="center">468.7594</td><td align="center">135.0173</td></tr><tr><td align="left">EMD</td><td align="center">824.3217</td><td align="center">470.1197</td><td align="center">135.2341</td></tr><tr><td align="left">Holt-Winters</td><td align="center">824.0651</td><td align="center">473.557</td><td align="center">218.0672</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Comparison of RMSE, MA, and MASE values for KLSE using the Holt-Winter, LLQ, EMD, and EMD-LLQ methods.

The Scientific World Journal

tab1

Table 1

Table 1: Application of Empirical Mode Decomposition with Local Linear Quantile Regression in Financial Time Series Forecasting 