Bayesian Estimation and Forecasting of Time Series in statsmodels
Chad Fulton
Statsmodels, a Python library for statistical and econometric analysis,
has traditionally focused on frequentist inference, including in its
models for time series data. This paper introduces the powerful features
for Bayesian inference of time series models that exist in statsmodels, with
applications to model fitting, forecasting, time series decomposition,
data simulation, and impulse response functions.
time series, forecasting, bayesian inference, Markov chain Monte Carlo, statsmodels
DOI10.25080/majora-212e5952-00d