Set of notebooks with various introductions and examples to several topics relating to forecasting and causal inference for the applied practitioner, including state space models with statsmodels (and pymc_experimental), bayesian inference with numpyro, and an evaluation of various time series utilities (i.e. orbit). Also compares some libraries around causal inference (i.e. CausalPy, tfcausalimpact)
My aim is to have a simple onramp onto Bayesian Structural Time Series by understanding components like:
- Concepts
- Bayesian Inference
- Bayesian Workflow
- Hierarchical Models
- State Space Models
- Causal Inference
- Computation
jax- Modeling and Inference in
numpyroandpymc arviz- Time Series utilities -
causalimpact,orbit,nixtla - Wrangling multidimensional data with
xarray