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We are looking for a close pythonian implementation of the r library bsts. To be precise, I'm looking for something that allows me to emulate the functionality of 'add_regressor' from fbprophet.

  1. Have already tried Pybsts (the kernel kept dying), and
  2. According to a thread on tensorflow_probability Github account, it doesn't support multivariate mode yet.

Any help would be appreciated. Thanks

kriti
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  • Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking. – Community Mar 13 '22 at 10:01

2 Answers2

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I recently wrote a version of R's bsts package in Python. It doesn't have all of bsts's features, but it does have options for level, trend, seasonality, and regression. The syntax closely follows statsmodels' UnobservedComponents module. You can find the code and description of the package here: https://github.com/devindg/pybuc.

Devin Garcia
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  • just dug into this package that Devin has made. It's super easy to use and understand the workflow. Def recommend trying this out. Great stuff @DevinGarcia – Timothy Mcwilliams May 25 '23 at 15:29
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This blog post from Tensorflow Probability shows how to add an exogenous regressor with the TFP structural time series tools. In particular, check out the usage of the temperature_effect variable in the Example: Forecasting Demand for Electricity section!

lunguini
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    [This](https://github.com/tensorflow/probability/blob/main/tensorflow_probability/examples/jupyter_notebooks/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand.ipynb) is from the Tensor Flow docs and is the same blog post with a little more explanation – SpacemanSpiff Sep 26 '22 at 19:52