Contents
What is a causal time series?
Causality means that an ARMA time series can be represented as a linear process. It was seen earlier in this section how an AR(1) process whose coefficient satisfies the condition |ϕ|<1 can be converted into a linear process.
What is invertible process?
Invertibility refers to linear stationary process which behaves like infinite representation of autoregressive. In other word, this is the property that possessed by a moving average process. Invertibility solves non-uniqueness of autocorrelation function of moving average.
What do you need to know about causal impact algorithm?
There are a few things to know about how Causal Impact algorithm works. As mentioned before, the core of the algorithm is to build a Bayesian structural time series model based on multiple Control groups and construct a synthetic time series baseline after adjusting the size difference between the Control groups and the Test group.
What are the concepts of causality for multiple time series?
Concepts of causality for multiple time series Among the many possible properties that have been hypothesized for cause–effect relationships, there are two properties that have been particularly fruitful in the context of time series. — Temporal precedence: a cause precedes its effects in time.
How is causalimpact used in structural time series?
In fact, the CausalImpact package selects the most predictive markets for the structural time series model using spike-and-slab priors (for more information, see the technical details below as well as [1]).
How does causalimpact work in a control market?
The CausalImpact package constructs a synthetic baseline for the post-intervention period based on a Bayesian structural time series model that incorporates multiple matching control markets as predictors, as well as other features of the time series. We can summarize this workflow as follows: