loongson/pypi/: pmdarima-1.8.5 metadata and description
Python's forecast::auto.arima equivalent
|keywords||arima timeseries forecasting pyramid pmdarima pyramid-arima scikit-learn statsmodels|
|maintainer||Taylor G. Smith|
Because this project isn't in the
no releases from root/pypi are included.
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
- 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 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 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
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) # predict N steps into the future # Visualize the forecasts (blue=train, green=forecasts) x = np.arange(y.shape) plt.plot(x[:150], train, c='blue') plt.plot(x[150:], forecasts, c='green') plt.show()
Fitting a more complex pipeline on the
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 (32 & 64-bit)
- 32-bit is only supported for 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
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
pmdarima documentation. If not, always
feel free to file an issue.