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    Home»Machine Learning»A Practical Guide to Time Series Model Explainability Using Darts | by Agreharshit | Jun, 2025
    Machine Learning

    A Practical Guide to Time Series Model Explainability Using Darts | by Agreharshit | Jun, 2025

    FinanceStarGateBy FinanceStarGateJune 12, 2025No Comments1 Min Read
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    A waterfall plot instance for mannequin exaplanability explanining mannequin characteristic significance

    In Darts, there’s a separate module that comes as much as clarify your mannequin and the forecast it makes. It appears up all of the vital options used for the mannequin coaching. It then helps in creating some stunning plots with that information to clarify the optimistic/damaging results of the options used for mannequin coaching.

    Mannequin explainability is among the options offered instantly by Shap in Darts, wrapped on high of your time sequence fashions to clarify the results of your forecast. it caculates the weighted values of all of the contributing options on totally different scales just like the previous covariates, goal column and future covaraite column to point out which column in what class is vital.

    The belief Shap has over the calculation of the characteristic significance with the goal is that the options are impartial of one another and don’t affect one another.

    Darts helps mannequin explainability just for the regression fashions, which implies these fashions that may have a look at the previous values after which forecast just for these. For instance, XGBOOST can solely assist mannequin explainability via Darts.



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