xgboost: give more importance to recent samples
Just add weights based on your time labels to your xgb.DMatrix.
The following example is written in R but the same principle applies to xgboost on Python or Julia.

data &lt;- data.frame(feature = rep(5, 5),
                   year = seq(2011, 2015), 
                   target = c(1, 0, 1, 0, 0))
weightsData &lt;- 1 + (data$year - max(data$year)) * 5 * 0.01

#Now create the xgboost matrix with your data and weights
xgbMatrix &lt;- xgb.DMatrix(as.matrix(data$feature), 
                         label = data$target, 
                         weight = weightsData)