augurs 0.1.2

Python bindings for the augurs time series library.

Python bindings to the augurs time series framework

Installation

Eventually wheels will be provided as part of GitHub releases and maybe even on PyPI. At that point it will be as easy as:

$ pip install augurs

Until then it's a bit more manual. You'll need maturin installed and a local copy of this repository. Then, from the crates/pyaugurs directory, with your virtualenv activated:

$ maturin build --release

You'll probably want numpy as well:

$ pip install numpy

Usage

Multiple Seasonal Trend Decomposition with LOESS (MSTL) models

import augurs as aug
import numpy as np

y = np.array([1.5, 3.0, 2.5, 4.2, 2.7, 1.9, 1.0, 1.2, 0.8])
periods = [3, 4]
# Use an AutoETS trend forecaster
model = aug.MSTL.ets(periods)
model.fit(y)
out_of_sample = model.predict(10, level=0.95)
print(out_of_sample.point())
print(out_of_sample.lower())
in_sample = model.predict_in_sample(level=0.95)

# Or use your own forecaster
class CustomForecaster:
    """See docs for more details on how to implement this."""    
    def fit(self, y: np.ndarray):
        pass
    def predict(self, horizon: int, level: float | None) -> aug.Forecast:
        return aug.Forecast(point=np.array([5.0, 6.0, 7.0]))
    def predict_in_sample(self, level: float | None) -> aug.Forecast:
        return aug.Forecast(point=y)
    ...

model = aug.MSTL.custom_trend(periods, aug.TrendModel(CustomForecaster()))
model.fit(y)
model.predict(10, level=0.95)
model.predict_in_sample(level=0.95)

Exponential smoothing models

import augurs as aug
import numpy as np

y = np.array([1.5, 3.0, 2.5, 4.2, 2.7, 1.9, 1.0, 1.2, 0.8])
model = aug.AutoETS(3, "ZZN")
model.fit(y)
model.predict(10, level=0.95)

More to come!