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)
- A single outcome series with trend and seasonal components
- Estimating the effect of a campaign or policy using untreated control series
- Wanting posterior credible intervals for forecasts
When to use something else
- Several treated and untreated units with panel data; use synthetic control or difference-in-differences
- Simple short-term forecasts without uncertainty modeling; use exponential smoothing
Assumptions and requirements
- Control series unaffected by the intervention
- Stable relationship between outcome and controls before the intervention
- Appropriate priors and state components
- Convergence of the MCMC sampler
What the analysis reports
- Decomposed trend and seasonal components
- Posterior inclusion probabilities of regressors
- Forecasts or counterfactual predictions with credible intervals
- Estimated absolute and relative effect with posterior probability
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
- SARIMAX Model
- Synthetic Control Method
- Interrupted Time Series Analysis
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.