Disclaimer: This project may contain experimental code and may not be ready for general use. Support and/or new releases may be limited.

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

import pandas as pd
import numpy as np

DATA_FILE = "data/iclaims_example.csv"
df = pd.read_csv(DATA_FILE, parse_dates=['week'])

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

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

lgt_full = LGTFull(
    response_col='claims',
    date_col='week',
    regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
    seasonality=52,
)
lgt_mcmc.fit(df=train_df)

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

# plot predictions
plot_predicted_data(
    training_actual_df=train_df, predicted_df=predicted_df,
    date_col=lgt_mcmc.date_col, actual_col=lgt_mcmc.response_col,
    pred_col=50, test_actual_df=test_df
)

Install from PyPi:

pip install orbit

Install from GitHub:

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