Question about suffleing time series data for classification problems

First, thank you for this amazing course, it is very helpful.

I would like to ask about shuffling. In Week 2, it is mentioned that "Shuffling us to rearrange the data so as not to accidentally introduce a sequence bias. Multiple runs will show the data in different arrangements because it gets shuffled randomly. "

Correct me if I am wrong, this does not apply for time series classification right? Because the problem I am working on is detecting certain patterns in EEG signals (brain electrical activity). If we apply suffleing for classification problems, i think this will change the original order of the data, thus not allowing the model to be able to learn the patterns of interest.

Thanks and regards,

Your understanding is correct. Shuffling time series data is a bad idea. But, that’s not what we’re doing here.
Shuffling is done after creating the x to y mappings. So, it’s safe to shuffle.
If you take a particular entry from the shuffled data, you’ll observe no randomness.


well noted. thank you so much for your answer!