Yes. Exactly what Luca said and what I said earlier.
There is temporal structure in your data. If you use k-fold cross
validation (or even shuffle the data) that destroys the temporal structure.
You want to make predictions for the future (the second file). You
should use a cross-validation method that tries to predict form the past
to the future, not that tries to predict arbitrary time points.
Otherwise, your results will be too optimistic, as you found.
On 02/09/2016 04:23 PM, muhammad waseem wrote:
I have it in separate file (csv). Actually, I have four years weather
data (hourly values in two files), I use 3 years (first file) worth of
data for training and one years worth of data (second file) for testing.
Am I doing it correctly? any ideas?
On Tue, Feb 9, 2016 at 9:01 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
How did you create the hold-out test data? Before or after shuffling?
On 02/09/2016 03:22 PM, muhammad waseem wrote:
Hi Andreas,
Thanks for your reply. I have already shuffled my data so it is
not in ordered now but still no luck. Any other suggestions?
On Tue, Feb 9, 2016 at 8:16 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
You should probably use a different cross-validation strategy
if your
data is ordered. This will give you more realistic
cross-validation results.
There was a time series CV object somewhere, and by now I
think we
should include it (this is the third time this comes up in
the last 3 days)
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