Metraipolis › archive › dc_472fc239f4bb

Bayesian structural time series — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Bayesian_structural_time_series
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-15T15:02:24.052Z
SHA-256 of the archived text: 5c153fba0310e152e010441ff16e900d8286a8c5da765174a39397e5e2839ce4
Stable URL of this page: https://metraipolis.com/documento/dc_472fc239f4bb

How to cite

Bayesian structural time series — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Bayesian_structural_time_series (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_472fc239f4bb (Metraipolis archive dc_472fc239f4bb, sha256 5c153fba0310e152e010441ff16e900d8286a8c5da765174a39397e5e2839ce4)

BibTeX

@misc{dc472fc239f4bb,
  title = {Bayesian structural time series — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Bayesian_structural_time_series},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_472fc239f4bb; sha256 5c153fba0310e152e010441ff16e900d8286a8c5da765174a39397e5e2839ce4},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Bayesian_structural_time_series. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data. The model has also promising application in the field of analytical marketing. In particular, it can be used in order to assess how much different marketing campaigns have contributed to the change in web search volumes, product sales, brand popularity and other relevant indicators. Difference-in-differences models and interrupted time series designs are alternatives to this approach. "In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls."

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; categoria «Bayesian statistics»).
Fonte: Wikipedia in inglese, voce «Bayesian structural time series» — https://en.wikipedia.org/wiki/Bayesian_structural_time_series
Wikidata Q25113119 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q25113119
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Bayesian_structural_time_series?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1307979873, consultata il 2026-10-15T14:06:05.843Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: ba4571f9d0d3135ea93b070e2abf33dea3ed9db492be1859e3b4edb88b5adff7