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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 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 under the hood, but is designed with an interface that's familiar to users coming from a scikit-learn background.



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:

pip install pmdarima


Pmdarima also has Mac and Linux builds available via conda and can be installed like so:

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 See that repo for further documentation on working with Pmdarima on Conda.

Quickstart Examples

Fitting a simple auto-ARIMA on the wineind dataset:

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')

Wineind example

Fitting a more complex pipeline on the sunspots dataset, serializing it, and then loading it from disk to make predictions:

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,

# 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)
# [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]


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:

# Legacy warning:
$ pip install pyramid-arima
# python -c 'import pyramid;'

However, this is not recommended.


All of your questions and more (including examples and guides) can be answered by the pmdarima documentation. If not, always feel free to file an issue.