# Python bindings to the augurs time series framework
## Installation
Wheels are provided on PyPI for all major platforms. Just run:
```shell
$ pip install augurs
```
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)
```
### Dynamic Time Warping
```python
import augurs as aug
a = np.array([1.0, 2.0, 3.0])
b = np.array([4.0, 5.0, 6.0])
dtw = aug.Dtw()
dist = dtw.distance(a, b)
dist_matrix = dtw.distance_matrix([a, b])
```
More to come!
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"description": "# Python bindings to the augurs time series framework\n\n## Installation\n\nWheels are provided on PyPI for all major platforms. Just run:\n\n```shell\n$ pip install augurs\n```\n\nYou'll probably want numpy as well:\n\n```shell\n$ pip install numpy\n```\n\n## Usage\n\n### Multiple Seasonal Trend Decomposition with LOESS (MSTL) models\n\n```python\nimport augurs as aug\nimport numpy as np\n\ny = np.array([1.5, 3.0, 2.5, 4.2, 2.7, 1.9, 1.0, 1.2, 0.8])\nperiods = [3, 4]\n# Use an AutoETS trend forecaster\nmodel = aug.MSTL.ets(periods)\nmodel.fit(y)\nout_of_sample = model.predict(10, level=0.95)\nprint(out_of_sample.point())\nprint(out_of_sample.lower())\nin_sample = model.predict_in_sample(level=0.95)\n\n# Or use your own forecaster\nclass CustomForecaster:\n \"\"\"See docs for more details on how to implement this.\"\"\" \n def fit(self, y: np.ndarray):\n pass\n def predict(self, horizon: int, level: float | None) -> aug.Forecast:\n return aug.Forecast(point=np.array([5.0, 6.0, 7.0]))\n def predict_in_sample(self, level: float | None) -> aug.Forecast:\n return aug.Forecast(point=y)\n ...\n\nmodel = aug.MSTL.custom_trend(periods, aug.TrendModel(CustomForecaster()))\nmodel.fit(y)\nmodel.predict(10, level=0.95)\nmodel.predict_in_sample(level=0.95)\n```\n\n### Exponential smoothing models\n\n```python\nimport augurs as aug\nimport numpy as np\n\ny = np.array([1.5, 3.0, 2.5, 4.2, 2.7, 1.9, 1.0, 1.2, 0.8])\nmodel = aug.AutoETS(3, \"ZZN\")\nmodel.fit(y)\nmodel.predict(10, level=0.95)\n```\n\n### Dynamic Time Warping\n\n```python\nimport augurs as aug\n\na = np.array([1.0, 2.0, 3.0])\nb = np.array([4.0, 5.0, 6.0])\ndtw = aug.Dtw()\ndist = dtw.distance(a, b)\ndist_matrix = dtw.distance_matrix([a, b])\n```\n\nMore to come!\n\n",
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