orbit-ml 1.0.5

Orbit is a package for bayesian time series modeling and inference.

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Disclaimer: Orbit requires PyStan as a system dependency. PyStan is licensed under GPLv3 , which is a free, copyleft license for software.

Orbit is a Python package for time series modeling and inference using Bayesian sampling methods for model estimation. It provides a familiar and intuitive initialize-fit-predict interface for working with time series tasks, while utilizing probabilistic modeling under the hood.

The initial release supports concrete implementation for the following models:

• Local Global Trend (LGT)

• Damped Local Trend (DLT)

Both models, which are variants of exponential smoothing, support seasonality and exogenous (time-independent) features.

The initial release 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

Installation

Prerequisites

Install from PyPi:

pip install orbit-ml

Install from GitHub:

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

Quick Start

Load data

import pandas as pd
import numpy as np

DATA_FILE = "./examples/data/iclaims_example.csv"
df = pd.read_csv(DATA_FILE, parse_dates=['week'])
df[['claims', 'trend.unemploy', 'trend.filling', 'trend.job']] =\
    df[['claims', 'trend.unemploy', 'trend.filling', 'trend.job']].apply(np.log)

test_size=52
train_df=df[:-test_size]
test_df=df[-test_size:]

Local-Global-Trend (LGT) Model with FULL Bayesian Prediction

from orbit.models.lgt import LGTFull
from orbit.diagnostics.plot import plot_predicted_data

lgt = LGTFull(
    response_col='claims',
    date_col='week',
    regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
    seasonality=52,
    prediction_percentiles=[5, 95],
)
lgt.fit(df=train_df)

# predicted df
predicted_df = lgt.predict(df=test_df)

# plot predictions
plot_predicted_data(
    training_actual_df=train_df, predicted_df=predicted_df,
    date_col=lgt.date_col, actual_col=lgt.response_col,
    pred_col='prediction', test_actual_df=test_df
)

References

Documentation

• Orbit API Documentation and Examples

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

• 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.

• 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.

• Taylor, S. J. and Letham, B. Forecasting at scale. The American Statistician, 72(1):37–45, 2018.

• Hewamalage, H., Bergmeir, C., and Bandara, K. Recurrent neural networks for time series forecasting: Current status and future directions. arXiv preprint arXiv:1909.00590, 2019.

Related projects

• Pyro

• Stan

• Rlgt