Methods · Time series · Bayesian time series

Bayesian Structural Time Series (BSTS)

Bayesian structural time series (BSTS) is a state-space model that decomposes a series into trend, seasonal and regression components and estimates them with Bayesian methods, often with spike-and-slab variable selection.

베이지안 구조 시계열(BSTS)은 시계열을 추세·계절·회귀 성분으로 분해하고 베이지안 방법으로 추정하는 상태공간 모형으로, 흔히 스파이크-앤-슬랩 변수선택을 함께 쓴다.

How do I forecast a time series or estimate the effect of an intervention with full uncertainty?

불확실성까지 포함하여 시계열을 예측하거나 개입 효과를 어떻게 추정하나요?

Bayesian structural time series decomposes a series into trend, seasonality and regression on control series, producing posterior forecasts with credible intervals. It fits forecasting and intervention analysis, where the pre-intervention model predicts a counterfactual for the post-intervention period.

When to use Bayesian Structural Time Series (BSTS)

When to use something else

Assumptions and requirements

What the analysis reports

How to report it

The BSTS model estimated a relative effect of [value]% on [outcome] (95% credible interval [[value], [value]]), with a posterior probability of a causal effect of [value].

Related methods

Run Bayesian Structural Time Series (BSTS) on Quorant

Data you need
a table (CSV or Excel) with a time variable and an outcome variable
How it is computed
Quorant computes a simplified form of this method; the report states the simplification.
Price
250 credits for one analysis up to 10,000 rows (complexity level 2); larger or wider data costs more. Optional methodologist review: 1200 credits.
Checking
Python results are cross-checked against R reference outputs in every deployment
Before you pay
A free pre-analysis diagnosis checks your data and gives an exact quote; nothing is deducted until you approve it.

Method ID Q53006 · Reviewed 2026-10-25