## 5 Methodology
### Data Modeling and Forecasting  
Once the seven reduced data subsets have been achieved, plus the original dataset, we will try to make a prediction of future values, taking into account the past time series collected in each dataset. We will attempt to forecast the complaints regarding GBV for a predictive horizon of six months but, in order to have the real data to evaluate/validate the prediction, we will apply a Cross-Validation (CV) method designed for time series [72]. We will train with a subset and then forecast the next six months/steps (for which the data is available but not included in the training dataset).  
For this purpose, we use the _time series Forecasting_ ([[http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka](http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka)]([http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka](http://wiki.pentaho.com/display/DATAMINING/Time+Series+Analysis+and+Forecasting+with+Weka))) module of WEKA (v. 1.027).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Search Method** & **Attribute Evaluator** & **Predictor** & **Acronym** \\ \hline \multirow{3}{*}{MOES} & \multirow{3}{*}{Wrapper} & Linear Regression & MOES-LR \\  & & Random Forest & MOES-RF \\  & & IBk & MOES-IBk \\ \hline \multirow{3}{*}{Ranker} & \multirow{3}{*}{Wrapper (Classifier)} & Linear Regression & Rnk-LR \\  & & Random Forest & Rnk-RF \\ \cline{1-1}  & & Relief & Rnk-Rlf \\ \cline{1-1}  & & PCA & Rnk-PCA \\ \hline \hline \end{tabular}
\end{table}
Table 2: Applied Features Selection techniques.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technique** & **Command** \\ \hline \multirow{3}{*}{MOES} & _weka.attributeSelection.MultiObjectiveEvolutionarySearch -generations 20 -population-size_ \\  & _100 -seed 1 -algorithm 0 -report-frequency 20 -log-file \"C:\textbackslash{}Program Files\textbackslash{}Weka-3-8\"_ \\ \hline \multirow{3}{*}{Ranker} & _weka.attributeSelection. Ranker -T -1. 8 -N -1_ \\ \hline \multirow{3}{*}{Wrapper LR} & _weka.attributeSelection.WrapperSubsetEval -B weda.classifiers.functions.LinearRegression -F_ \\  & _5 -T 0.01 -R 1 -E RMSE - 5 0 -R 1.0E-8 -num-decimal-places 4_ \\ \hline \multirow{3}{*}{Wrapper RF} & _weka.attributeSelection.WrapperSubsetEval -B weda.classifiers.trees.RandomForest -F 5 -T_ \\  & _0.01 -R 1 -E RMSE - -P 100 -100 -num-slots 1 -K 0 -M 1.0 -V 0.001 -S 1_ \\  & _-num-decimal-places 4_ \\ \hline \multirow{3}{*}{Wrapper IBk} & _weka.attributeSelection.WrapperSubsetEval -B weda.classifiers.lazy.IBk -F 5 -T 0.01 -R 1 -E_ \\  & _RMSE -K 1 -W 0 -A -\"weka.core.neighboursearch.LinearNNSearch -A_ \\  & \textbackslash{}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\textFor each dataset, we use the following approaches as forecasting algorithms, always indicating the accuracy in terms of RMSE.  
- Linear Regression (LR).  
- Support Vector Machines (SVM).  
- Random Forest (RF).  
- Gaussian Process (GP).  
A description of each method can be found in Section 3.2. Table 4 expresses the WEKA commands and the parameters of each technique.