Feature Selection Methods for Imputation Missing Values of Time Series Data using Data Mining
Keywords:
Feature Selection, Data Imputation, Missing Values, Time Series Data, Data MiningAbstract
This research proposes a feature selection method for time series data of Royal Irrigation Department, Royal Thai Army, over a period of 5 year consisting of 12 variables. The proposed feature selection is a voting scheme based on 5 techniques: Principal Components Analysis (PCA), Correlation-based Feature Selection (CFS), ReliefF algorithm (ReliefF), Gain Ratio (GR), and Information Gain (IG) Multilayer Perceptron neural network was used as the missing values imputation model. To test the efficiency of the proposed method, the researchers used the complete data to randomly force the data to be missing for 5, 10, 15, 20, 25 and 30%, respectively. From the experiments, 9 out of 12 variables that are variables 1, 2, 3, 4, 5, 6, 7, 8 and 10, were selected. In addition, 10 Multilayer Perceptron neural network models that are 9-3-1, 9-5-1, 9-10-1, 9-15-1, 9-20-1, 9-25-1, 9-30-1, 9-35-1, 9-40-1 and 9-45-1 (inputs-hidden neurons-outputs) were used in the experiments. Using 10-fold-cross-validation, the best performance was the 9-30-1 model, yielding the lowest MSE equaled 0.669.
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