Variables used as inputs.

<p>Variables used as inputs.</p

The input and output data of AICA-SVR model.

<p>The input and output data of AICA-SVR model.</p

Variables used as inputs.

<p><i>I</i>_<i>1</i> to <i>I</i>_<i>36</i> 36 variables are selected as the inputs of the forecasting model. The name and description of the variables are shown in the 1^st column and the 2^nd column, respectively.</p><p>Variables used as inputs.</p

Diagram of (2D)²PCA+RBFNN forecasting model.

<p>The model is divided five modules, including the database of stock market, variables calculated module, sliding window, dimension reduction module and RBFNN predictor.</p

Measure indicators.

<p>Measure indicators.</p

The actual Shanghai stock market index and its predicted values from ICA-CCA-SVR, MICA-SVR, and A ICA-SVR.

<p>The actual Shanghai stock market index and its predicted values from ICA-CCA-SVR, MICA-SVR, and A ICA-SVR.</p

Measure index on the set of Shanghai stock market index.

<p>Measure index on the set of Shanghai stock market index.</p

The actual Dow Jones index and its predicted values from ICA-CCA-SVR, MICA-SVR, and AICA-SVR.

<p>The actual Dow Jones index and its predicted values from ICA-CCA-SVR, MICA-SVR, and AICA-SVR.</p

Multi-variable ICA regression model (MICA-SVR).

<p>Multi-variable ICA regression model (MICA-SVR).</p

Shows the curve versus the variation of dimensions and the proposed method consistently outperforms the other methods in the Dow Jones index.

<p>Shows the curve versus the variation of dimensions and the proposed method consistently outperforms the other methods in the Dow Jones index.</p