ARU embeds closed-form local linear models based on conditional Gaussian sufficient statistics into deep global forecasting networks for efficient streaming per-series adaptation.
Effective Bayesian Modeling of Groups of Related Count Time Series
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abstract
Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Streaming Adaptation of Deep Forecasting Models using Adaptive Recurrent Units
ARU embeds closed-form local linear models based on conditional Gaussian sufficient statistics into deep global forecasting networks for efficient streaming per-series adaptation.