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question #1
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Hi, thanks what do you mean for 'casual' ? |
Marco, Sometime smoother give you a no casual signal ... the output depends upon future inputs.. / data forward snooping. |
All are in this sense casual. The way they are casual (the strength of casuality) depends on the algorithm adopted. To make them not casual you have to 'censor' the future and iterate the smoothing step by step (or by data intervals). if you want to iter step by step you can use the WindowWrapper which is compatible whit every smoother for 1D series |
Thanks your advices ! |
Hi Marco
First thank you for your python package !
Among all the smoother of the package which one is casual ? or are they all no casual ?
Regards
Ludo
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