Predicting the present with Bayesian structural time series

By Steven L. Scott, Hal R. Varian. Int. J. Math. Model. Numer. Optimisation, 2014

403 citations recorded by the source corpus.

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Text as Data (2017, methodology): “In MCMC sampling, as in the Bayesian regression of Scott and Varian (2014) or the topic modeling of Griffiths and Steyvers (2004), the software returns samples from the posterior and thus inference is immediately available.”
Forecasting Big Time Series: Old and New (2018, neutral): “In particular, we focus on the exponential smoothing models (Innovation state space models (ISSM)) [11] and structural time series models [8, 5, 30] along with their Bayesian counterparts [32, 31].”
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data (2019, background): “The ﬁrst category of statistics baselines includes: STRX is the structural time series model with exogenous variables (Scott & Varian, 2014; Radinsky et al., 2012).”
GluonTS: Probabilistic Time Series Models in Python (2019, methodology): “The R-forecast package (Hyndman and Khandakar, 2008) and other packages such as (Taylor and Letham, 2017; Scott and Varian, 2014) provide a plethora of models and tooling for classical forecasting methods.”
Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior (2016, background): “Scott and Varian (2014) elicited an informative distribution for the error variance, σ 2, based on elicitation of the expected R2, and the response.”