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.arimafunctionality - 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 pmdarimaPmdarima 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 pmdarimaNote: 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.
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')
plt.show()
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,
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]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.
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