A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls

25 Jun 2019Konstantin MishchenkoMallory MontgomeryFederico Vaggi

When forecasting time series with a hierarchical structure, the existing state of the art is to forecast each time series independently, and, in a post-treatment step, to reconcile the time series in a way that respects the hierarchy (Hyndman et al., 2011; Wickramasuriya et al., 2018). We propose a new loss function that can be incorporated into any maximum likelihood objective with hierarchical data, resulting in reconciled estimates with confidence intervals that correctly account for additional uncertainty due to imperfect reconciliation... (read more)

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