Disclaimer

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:

HTML (stable): https://orbit-ml.readthedocs.io/en/stable/
HTML (latest): https://orbit-ml.readthedocs.io/en/latest/

Currently, it supports concrete implementations for the following models:

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

It also supports the following sampling/optimization methods for model estimation/inferences:

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

Installation

Installing Stable Release

Install from PyPi:

$ pip install orbit-ml

Install from source:

$ git clone https://github.com/uber/orbit.git
$ cd orbit
$ pip install -r requirements.txt
$ pip install .

Installing from Dev Branch

$ pip install git+https://github.com/uber/orbit.git@dev

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

FULL Bayesian Prediction

from orbit.utils.dataset import load_iclaims
from orbit.models import DLT
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 = DLT(
  response_col='claims', date_col='week',
  regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
  seasonality=52,
)
dlt.fit(df=train_df)

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

plot_predicted_data(
  training_actual_df=train_df, predicted_df=predicted_df,
  date_col=dlt.date_col, actual_col=dlt.response_col,
  test_actual_df=test_df
)

Demo

Forecasting / Nowcasting with Regression in DLT (based on v1.0.13):

Backtest on M3 Data:

More examples can be found under tutorials and examples.

Contributing

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

Versioning

We document versions and changes in our changelog.

References

Presentations

Check out the deck for a meet-up event in July 2021.

Citation

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

Orbit: Probabilistic Forecast with Exponential Smoothing

Bibtex:

@misc{
    ng2020orbit,
    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}
}

Papers

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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