pmdarima 2.1.1
Python's forecast::auto.arima equivalent
Requires Python: >=3.10
Keywords: arima, timeseries, forecasting, pyramid, pmdarima, pyramid-arima, scikit-learn, statsmodels

# pmdarima

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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 3.10+ 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.