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The default page of the repo is ondev branch. To install the dev version, please check the sectionInstalling from Dev Branch. If you are looking for astable version, please refer to themaster branchhere.

This project

• is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
• requirescmdstanpy as one of the core dependencies for Bayesian sampling.

Orbit is a Python package for Bayesian time series forecasting and inference. It provides afamiliar 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 approximatedistribution

Install the library either from PyPi or from the source withpip.Alternatively, you can also install it from Anaconda withconda:

With pip

1. Installing from PyPI

   $ pip install orbit-ml

2. Install from source

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

With conda

The library can be installed from the conda-forge channel using conda.

$ conda install -c conda-forge orbit-ml

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

fromorbit.utils.datasetimportload_iclaimsfromorbit.modelsimportDLTfromorbit.diagnostics.plotimportplot_predicted_data# log-transformed datadf=load_iclaims()# train-test splittest_size=52train_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 framepredicted_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)

Nowcasting with Regression in DLT:

Backtest on M3 Data:

More examples can be found undertutorialsandexamples.

We welcome community contributors to the project. Before you start, please read ourcode of conduct and check outcontributing guidelines first.

We document versions and changes in ourchangelog.

Check out the ongoingdeck for scope and roadmap of the project. An older deck used in themeet-up during July 2021 can also be foundhere.

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}}

• 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 LearningResearch, 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.

• Pyro
• Stan
• Rlgt
• forecast
• prophet