Fill gap time series #284
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Hello, |
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Hi @spetton , Currently, skforecast models require the time series to be complete. You could try to train a forecaster with the longest portion of your time series without missing values, and then predict the missing gaps. In the next release (0.6.0), all forecasters will allow the use of weights in the model fitting. Therefore, you can impute the missing values with a random value and give a weight of 0 to ignore them during the training. You can install the pre-release with Hope this helps! |
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Hi @spetton, Another option is to fill in the gaps with a rolling average, train the model, predict the gaps, and then retrain the model with the filled data. From my point of view, this might make sense if you have some exogenous variables that affect your forecaster. Best, |
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Hi @spetton ,
Currently, skforecast models require the time series to be complete. You could try to train a forecaster with the longest portion of your time series without missing values, and then predict the missing gaps.
In the next release (0.6.0), all forecasters will allow the use of weights in the model fitting. Therefore, you can impute the missing values with a random value and give a weight of 0 to ignore them during the training. You can install the pre-release with
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
.Hope this helps!