# 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:
```shell
$ 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:
```shell
$ maturin build --release
```
You'll probably want numpy as well:
```shell
$ pip install numpy
```
## Usage
### Multiple Seasonal Trend Decomposition with LOESS (MSTL) models
```python
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
```python
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!
[maturin]: https://www.maturin.rs/