Personally I think that random forest should not be used for time series
data unless the data is supposed to have some sort of periodicity. This is
because random forest is a sort of local estimator. It's not effective if
new samples are outside of the hypercube defined by the training data.
This is quite common in time series. If I were you I would try something
like linear regression or extreme learning machine. If you are interested
in extreme learning machine there should be a PR on scikit-learn (I wrote a
simple paper with a simple introduction to ELM: "Extreme learning machines
for virtual metrology and etch rate prediction". Maybe this can help you
.
On Tue, Feb 9, 2016, 9:41 PM Andreas Mueller <t3k...@gmail.com> wrote:
> 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>
> 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> 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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