I'm fitting some machine learning algorithms (e.g. SVM) on my panel data. It's taking too long for my entire dataset, so I'm considering generating smaller samples from bootstrapping then fit the SVM in parallel.
At first glance, it may seem like by bootstrapping I will destroy the time-dimension of my data. However, if I include variable lags and moving averages, can I just bootstrap as normal (i.e. considering each observation as independent)?