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BATS and TBATS for time series forecasting

Project description

BATS and TBATS time series forecasting

Package provides BATS and TBATS time series forecasting methods described in:

De Livera, A.M., Hyndman, R.J., & Snyder, R. D. (2011), Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.


From pypi:

pip install tbats

Import via:

from tbats import BATS, TBATS

Minimal working example:

from tbats import TBATS
import numpy as np

# required on windows for multi-processing,
# see
if __name__ == '__main__':
    t = np.array(range(0, 160))
    y = 5 * np.sin(t * 2 * np.pi / 7) + 2 * np.cos(t * 2 * np.pi / 30.5) + \
        ((t / 20) ** 1.5 + np.random.normal(size=160) * t / 50) + 10

    # Create estimator
    estimator = TBATS(seasonal_periods=[14, 30.5])

    # Fit model
    fitted_model =

    # Forecast 14 steps ahead
    y_forecasted = fitted_model.forecast(steps=14)

    # Summarize fitted model

Reading model details

# Time series analysis
print(fitted_model.y_hat) # in sample prediction
print(fitted_model.resid) # in sample residuals

# Reading model parameters

See examples directory for more details

For Contributors

Building package:

pip install -e .[dev]

Unit and integration tests:

python test

R forecast package comparison tests. Those DO NOT RUN with default test command, you need R forecast package installed:

python test_r

Comparison to R implementation

Python implementation is meant to be as much as possible equivalent to R implementation in forecast package.

Project details

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