I got a question about splitting the data into a training and test set in Time Series tasks. I know that the data can't be shuffled, because its important to keep the time nature of the data, so we do not create the scenario where we are able to look into the future. However, when I shuffle the data ( for experimenting ), I get a ridiculously high R-Squared score. And yes, the R Squared is evaluated with the test set. Can someone maybe simply explain why this is the case? Why does shuffling train and test data in time series produce a high R-Squared score? My guess is that it has something to the with the trend of the time series, but i am not sure. I am just asking out of curiosity, thanks !
It really depends upon your problem. If:
Hope this helps!
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