# pmdarima
[](https://badge.fury.io/py/pmdarima)
[](https://circleci.com/gh/alkaline-ml/pmdarima)
[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster)
[](https://codecov.io/gh/alkaline-ml/pmdarima)
Pmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical
library designed to fill the void in Python's time series analysis capabilities. This includes:
* The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality
* A collection of statistical tests of stationarity and seasonality
* Time series utilities, such as differencing and inverse differencing
* Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations
* Seasonal time series decompositions
* Cross-validation utilities
* A rich collection of built-in time series datasets for prototyping and examples
* Scikit-learn-esque pipelines to consolidate your estimators and promote productionization
Pmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels)
under the hood, but is designed with an interface that's familiar to users coming
from a scikit-learn background.
## Installation
### pip
Pmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi
under the package name `pmdarima` and can be downloaded via `pip`:
```bash
pip install pmdarima
```
### conda
Pmdarima also has Mac and Linux builds available via `conda` and can be installed like so:
```bash
conda config --add channels conda-forge
conda config --set channel_priority strict
conda install pmdarima
```
**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock.
See that repo for further documentation on working with Pmdarima on Conda.
## Quickstart Examples
Fitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset:
```python
import pmdarima as pm
from pmdarima.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
# Load/split your data
y = pm.datasets.load_wineind()
train, test = train_test_split(y, train_size=150)
# Fit your model
model = pm.auto_arima(train, seasonal=True, m=12)
# make your forecasts
forecasts = model.predict(test.shape[0]) # predict N steps into the future
# Visualize the forecasts (blue=train, green=forecasts)
x = np.arange(y.shape[0])
plt.plot(x[:150], train, c='blue')
plt.plot(x[150:], forecasts, c='green')
plt.show()
```
<img src="http://alkaline-ml.com/img/static/pmdarima_readme_example1.png" alt="Wineind example"/>
Fitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset,
serializing it, and then loading it from disk to make predictions:
```python
import pmdarima as pm
from pmdarima.model_selection import train_test_split
from pmdarima.pipeline import Pipeline
from pmdarima.preprocessing import BoxCoxEndogTransformer
import pickle
# Load/split your data
y = pm.datasets.load_sunspots()
train, test = train_test_split(y, train_size=2700)
# Define and fit your pipeline
pipeline = Pipeline([
('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values
('arima', pm.AutoARIMA(seasonal=True, m=12,
suppress_warnings=True,
trace=True))
])
pipeline.fit(train)
# Serialize your model just like you would in scikit:
with open('model.pkl', 'wb') as pkl:
pickle.dump(pipeline, pkl)
# Load it and make predictions seamlessly:
with open('model.pkl', 'rb') as pkl:
mod = pickle.load(pkl)
print(mod.predict(15))
# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043
# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243
# 17.8250318 17.47750614 17.16803394]
```
### Availability
`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms:
* Mac (64-bit)
* Linux (64-bit manylinux)
* Windows (64-bit)
* 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10
If a wheel doesn't exist for your platform, you can still `pip install` and it
will build from the source distribution tarball, however you'll need `cython>=0.29`
and `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source.
Note that legacy versions (<1.0.0) are available under the name
"`pyramid-arima`" and can be pip installed via:
```bash
# Legacy warning:
$ pip install pyramid-arima
# python -c 'import pyramid;'
```
However, this is not recommended.
## Documentation
All of your questions and more (including examples and guides) can be answered by
the [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). If not, always
feel free to file an issue.
Raw data
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"description": "# pmdarima\n\n[](https://badge.fury.io/py/pmdarima)\n[](https://circleci.com/gh/alkaline-ml/pmdarima)\n[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster)\n[](https://codecov.io/gh/alkaline-ml/pmdarima)\n\n\n\n\nPmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical\nlibrary designed to fill the void in Python's time series analysis capabilities. This includes:\n\n * The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality\n * A collection of statistical tests of stationarity and seasonality\n * Time series utilities, such as differencing and inverse differencing\n * Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations\n * Seasonal time series decompositions\n * Cross-validation utilities\n * A rich collection of built-in time series datasets for prototyping and examples\n * Scikit-learn-esque pipelines to consolidate your estimators and promote productionization\n \nPmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels)\nunder the hood, but is designed with an interface that's familiar to users coming\nfrom a scikit-learn background.\n\n## Installation\n\n### pip\n\nPmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi\nunder the package name `pmdarima` and can be downloaded via `pip`:\n\n```bash\npip install pmdarima\n```\n\n### conda\n\nPmdarima also has Mac and Linux builds available via `conda` and can be installed like so:\n\n```bash\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda install pmdarima\n```\n\n**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock.\nSee that repo for further documentation on working with Pmdarima on Conda.\n\n## Quickstart Examples\n\nFitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset:\n\n```python\nimport pmdarima as pm\nfrom pmdarima.model_selection import train_test_split\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load/split your data\ny = pm.datasets.load_wineind()\ntrain, test = train_test_split(y, train_size=150)\n\n# Fit your model\nmodel = pm.auto_arima(train, seasonal=True, m=12)\n\n# make your forecasts\nforecasts = model.predict(test.shape[0]) # predict N steps into the future\n\n# Visualize the forecasts (blue=train, green=forecasts)\nx = np.arange(y.shape[0])\nplt.plot(x[:150], train, c='blue')\nplt.plot(x[150:], forecasts, c='green')\nplt.show()\n```\n\n<img src=\"http://alkaline-ml.com/img/static/pmdarima_readme_example1.png\" alt=\"Wineind example\"/>\n\n\nFitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset,\nserializing it, and then loading it from disk to make predictions:\n\n```python\nimport pmdarima as pm\nfrom pmdarima.model_selection import train_test_split\nfrom pmdarima.pipeline import Pipeline\nfrom pmdarima.preprocessing import BoxCoxEndogTransformer\nimport pickle\n\n# Load/split your data\ny = pm.datasets.load_sunspots()\ntrain, test = train_test_split(y, train_size=2700)\n\n# Define and fit your pipeline\npipeline = Pipeline([\n ('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values\n ('arima', pm.AutoARIMA(seasonal=True, m=12,\n suppress_warnings=True,\n trace=True))\n])\n\npipeline.fit(train)\n\n# Serialize your model just like you would in scikit:\nwith open('model.pkl', 'wb') as pkl:\n pickle.dump(pipeline, pkl)\n \n# Load it and make predictions seamlessly:\nwith open('model.pkl', 'rb') as pkl:\n mod = pickle.load(pkl)\n print(mod.predict(15))\n# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043\n# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243\n# 17.8250318 17.47750614 17.16803394]\n```\n\n\n### Availability\n\n`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms:\n\n* Mac (64-bit)\n* Linux (64-bit manylinux)\n* Windows (64-bit)\n * 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10\n\nIf a wheel doesn't exist for your platform, you can still `pip install` and it\nwill build from the source distribution tarball, however you'll need `cython>=0.29`\nand `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source.\n\nNote that legacy versions (<1.0.0) are available under the name\n\"`pyramid-arima`\" and can be pip installed via:\n\n```bash\n# Legacy warning:\n$ pip install pyramid-arima\n# python -c 'import pyramid;'\n```\n\nHowever, this is not recommended.\n\n## Documentation\n\nAll of your questions and more (including examples and guides) can be answered by\nthe [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). 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