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Manually set horizon weights to different windows #939
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Thanks for the suggestion. While we consider it, options to account for different sample importances are:
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Description
To my understanding, currently the implementation of horizon weights is by adding a fixed horizon_weight list when initializing the loss function.
However in the case I'm dealing with, the loss during holidays is considered more important, not the distance between predicted timestamp and current timestamp.
I add a column to the input DataFrame representing the weight of each timestamp and try to modify the base window class to extract weights for different time windows. Is there any way to adding weight more easily?
Thanks
Use case
Setting different horizon weight to each window by simply adding a weight column in the input DataFrame.
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