  A structural time series model additively decomposes into generative,
semantically-meaningful components, each of which depends on a vector of
parameters. We demonstrate that considering each generative component together
with its vector of parameters as a single latent structural time series node
can simplify reasoning about collections of structural time series components.
We then introduce a formal grammar over structural time series nodes and
parameter vectors. Valid sentences in the grammar can be interpreted as
generative structural time series models. An extension of the grammar can also
express structural time series models that include changepoints, though these
models are necessarily not generative. We demonstrate a preliminary
implementation of the language generated by this grammar. We close with a
discussion of possible future work.
