Comments (5)
I see. In the current version it is not possible to face this problem, but in skforecast 0.12 (hopefully released in 2 weeks) the ForecasterAutoregMultiSeries
will be able to handle this situation. I will come back to this issue when the release is ready.
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Hello @davidusb-geek
It looks like a multi-series problem, but some preprocessing needs to be done.
Could you describe the problem a little more, what are the target variables and what are the exogenous variables?
Thanks
from skforecast.
Hello @davidusb-geek
It looks like a multi-series problem, but some preprocessing needs to be done.
Could you describe the problem a little more, what are the target variables and what are the exogenous variables?
Thanks
The problem is exactly like in the image I posted.
I have multiple time series instances (sometimes called samples), and each instance or sample has the same length with a timestamp. Well in my case the timestamp is already a DateTimeIndex
so well fitted for skforecast
. Then for each of these instances/samples I have features variables (var_0
, var1
, ...) and a target. All these variables are float, no NaN's.
That's it. I want to forecast the target using the information from all the features.
from skforecast.
Great to hear that Javier.
Kudos for all this nice work!
from skforecast.
Hello @davidusb-geek,
The functionality to include series-specific exogenous variables in ForecasterMultiSeries is now available in skforecast 0.12.0:
https://skforecast.org/latest/user_guides/multi-series-with-different-length-and-different_exog
We will improve the user guide in the coming days. 😄
Hope it helps!
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Related Issues (20)
- Custom predictors are inefficient for window features HOT 1
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- Feature request: Add ability to skip steps in backtesting HOT 6
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