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.

中文翻译：

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可变块上的结构时间序列语法

结构时间序列模型将附加地分解为生成性的，语义上有意义的组件，每个组件都取决于参数向量。我们证明，将每个生成分量及其参数向量视为单个潜在结构时间序列节点可以简化关于结构时间序列分量集合的推理。然后，我们介绍有关结构时间序列节点和参数向量的形式语法。语法中的有效句子可以解释为生成结构时间序列模型。语法的扩展还可以表示包含变化点的结构时间序列模型，尽管这些模型不一定是生成的。我们演示了此语法生成的语言的初步实现。最后，我们讨论了未来可能的工作。