Hello, I would like to perform feature selection in a set of features that are used for regression. Especially, those features correspond to the previous day values (e.g Lag24,Lag25,Lag26...) where lag24 is the value 24 hour before. The target variable y is the value at the current time (Using past day features in order to predict the next day). I am currently using SVM from the e1071 package. However, I found that when I remove some features the svm performance is increased? Is there any way so to do feature selection using the SVM? (1). Also I have tried to use the glmnet package for doing regression but with no luck. The purpose for using the glmnet was the LASSO penalizing on the model. Can I do something similar using e1071? (2) . I am not using any penalizing in e1071 so maybe this is an issue. Also could you please list me 2-3 packages used for non-linear regression. (3) Currently I am aware of: e1071 penalizedSVM randomForrest RSNNS (elman, jordan neural networks) forecast (For using ARIMA) glmnet (No luck) I have tried may of these but without very good results even if my data have a periodicity (25% Mean Relative Absolute Error). For feature selection until now I use the corrgrams function that returns the correlation of the features. My Questions have the symbol ( Question Id). Thank you for your support. [[alternative HTML version deleted]]