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This project

  • is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
  • requires PyStan as a system dependency. PyStan is licensed under GPLv3, which is a free, copyleft license for software.

Orbit: A Python Package for Bayesian Forecasting

Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.

For details, check out our documentation and tutorials:

Currently, it supports concrete implementations for the following models:

  • Exponential Smoothing (ETS)
  • Damped Local Trend (DLT)
  • Local Global Trend (LGT)
  • Kernel Time-based Regression (KTR-Lite)

It also supports the following sampling methods for model estimation:

  • Markov-Chain Monte Carlo (MCMC) as a full sampling method
  • Maximum a Posteriori (MAP) as a point estimate method
  • Variational Inference (VI) as a hybrid-sampling method on approximate distribution


Installing Stable Release

Install from PyPi:

$ pip install orbit-ml

Install from source:

$ git clone
$ cd orbit
$ pip install -r requirements.txt
$ pip install .

Installing from Dev Branch

$ pip install git+[email protected]

Quick Start with Damped-Local-Trend (DLT) Model

FULL Bayesian Prediction

from orbit.utils.dataset import load_iclaims
from orbit.models.dlt import DLTFull
from orbit.diagnostics.plot import plot_predicted_data

# log-transformed data
df = load_iclaims()
# train-test split
test_size = 52
train_df = df[:-test_size]
test_df = df[-test_size:]

dlt = DLTFull(
    response_col='claims', date_col='week',
    regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],

# outcomes data frame
predicted_df = dlt.predict(df=test_df)

    training_actual_df=train_df, predicted_df=predicted_df,
    date_col=dlt.date_col, actual_col=dlt.response_col,



Forecasting / Nowcasting with Regression in DLT:

Open All Collab

Backtest on M3 Data:

Open All Collab

More examples can be found under tutorials and examples.


We welcome community contributors to the project. Before you start, please read our code of conduct and check out contributing guidelines first.


We document versions and changes in our changelog.



Check out the deck we presented in a meet up event in Jul. 2021.


To cite Orbit in publications, refer to the following whitepaper:

Orbit: Probabilistic Forecast with Exponential Smoothing


    title={Orbit: Probabilistic Forecast with Exponential Smoothing},
    author={Edwin Ng,
        Zhishi Wang,
        Huigang Chen,
        Steve Yang,
        Slawek Smyl},
    year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}


  • Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D. Pyro: Deep universal probabilistic programming. The Journal of Machine Learning Research, 20(1):973–978, 2019.
  • Hoffman, M.D. and Gelman, A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1), pp.1593-1623, 2014.
  • Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. Forecasting with exponential smoothing: the state space approach. Springer Science & Business Media, 2008.
  • Smyl, S. Zhang, Q. Fitting and Extending Exponential Smoothing Models with Stan. International Symposium on Forecasting, 2015.

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