Thanks Luca and Andreas, the idea behind this is to predict a weather
parameter using some other parameters. You still think it will be difficult
to solve with Random Forest as it is not really time series. I get good
training results (with high max_depth) but not very good for the testing
dataset, meaning the regressor is unable to generalise.
What about gradient boosting regressor, is this suitable?
Thanks
Kindest Regards
Waseem
On Tue, Feb 9, 2016 at 10:00 PM, Luca Puggini <lucapug...@gmail.com> wrote:
> 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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