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Bayesian Structural Time Series/Evidence
Method evidence record

Bayesian Structural Time Series

Bayesian Structural Time Series (BSTS) is a state-space modelling framework, introduced by Scott and Varian (2014), that decomposes a time series into additive components — trend, seasonality, and regression — and estimates them jointly through Bayesian inference. It underpins Google's CausalImpact library and is a powerful tool for both forecasting and counterfactual causal analysis of interventions.

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Bayesian Structural Time Series Model
Taxonomic method record · bayesian / bayesian
  • Scott, S. L. & Varian, H. R. (2014). Predicting the Present with Bayesian Structural Time Series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4–23. · DOI 10.1504/IJMMNO.2014.059942
  • Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N. & Scott, S. L. (2015). Inferring Causal Impact Using Bayesian Structural Time-Series Models. Annals of Applied Statistics, 9(1), 247–274. · DOI 10.1214/14-AOAS788
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

See alsoARIMAmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.See alsoInterrupted Time Seriesmachine-suggested · Relational suggestion, not evidence.Same method familyMCMCmachine-suggested · Relational suggestion, not evidence.See alsoState Space Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

2 recorded citations, copied from the method source record.

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