195 lines
7.7 KiB
Plaintext
195 lines
7.7 KiB
Plaintext
Metadata-Version: 2.1
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Name: pmdarima
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Version: 2.0.4
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Summary: Python's forecast::auto.arima equivalent
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Home-page: http://alkaline-ml.com/pmdarima
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Download-URL: https://pypi.org/project/pmdarima/#files
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Maintainer: Taylor G. Smith
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Maintainer-email: taylor.smith@alkaline-ml.com
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License: MIT
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Project-URL: Bug Tracker, https://github.com/alkaline-ml/pmdarima/issues
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Project-URL: Documentation, http://alkaline-ml.com/pmdarima
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Project-URL: Source Code, https://github.com/alkaline-ml/pmdarima
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Keywords: arima timeseries forecasting pyramid pmdarima pyramid-arima scikit-learn statsmodels
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Classifier: Intended Audience :: Science/Research
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Classifier: Intended Audience :: Developers
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Classifier: Intended Audience :: Financial and Insurance Industry
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Classifier: Programming Language :: C
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Classifier: Programming Language :: Python
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Classifier: Topic :: Software Development
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Classifier: Topic :: Scientific/Engineering
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Classifier: Operating System :: Microsoft :: Windows
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Classifier: Operating System :: POSIX
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Classifier: Operating System :: Unix
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Classifier: Operating System :: MacOS
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.7
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Programming Language :: Python :: Implementation :: CPython
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Requires-Python: >=3.7
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Description-Content-Type: text/markdown
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License-File: LICENSE
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License-File: AUTHORS.md
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Requires-Dist: joblib >=0.11
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Requires-Dist: Cython !=0.29.18,!=0.29.31,>=0.29
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Requires-Dist: numpy >=1.21.2
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Requires-Dist: pandas >=0.19
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Requires-Dist: scikit-learn >=0.22
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Requires-Dist: scipy >=1.3.2
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Requires-Dist: statsmodels >=0.13.2
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Requires-Dist: urllib3
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Requires-Dist: setuptools !=50.0.0,>=38.6.0
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Requires-Dist: packaging >=17.1
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# pmdarima
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[](https://badge.fury.io/py/pmdarima)
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[](https://circleci.com/gh/alkaline-ml/pmdarima)
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[](https://github.com/alkaline-ml/pmdarima/actions?query=workflow%3A%22Mac+and+Windows+Builds%22+branch%3Amaster)
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[](https://codecov.io/gh/alkaline-ml/pmdarima)
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Pmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical
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library designed to fill the void in Python's time series analysis capabilities. This includes:
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* The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality
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* A collection of statistical tests of stationarity and seasonality
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* Time series utilities, such as differencing and inverse differencing
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* Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations
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* Seasonal time series decompositions
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* Cross-validation utilities
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* A rich collection of built-in time series datasets for prototyping and examples
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* Scikit-learn-esque pipelines to consolidate your estimators and promote productionization
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Pmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels)
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under the hood, but is designed with an interface that's familiar to users coming
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from a scikit-learn background.
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## Installation
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### pip
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Pmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi
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under the package name `pmdarima` and can be downloaded via `pip`:
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```bash
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pip install pmdarima
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```
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### conda
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Pmdarima also has Mac and Linux builds available via `conda` and can be installed like so:
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```bash
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conda config --add channels conda-forge
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conda config --set channel_priority strict
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conda install pmdarima
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```
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**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock.
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See that repo for further documentation on working with Pmdarima on Conda.
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## Quickstart Examples
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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:
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```python
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import pmdarima as pm
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from pmdarima.model_selection import train_test_split
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import numpy as np
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import matplotlib.pyplot as plt
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# Load/split your data
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y = pm.datasets.load_wineind()
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train, test = train_test_split(y, train_size=150)
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# Fit your model
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model = pm.auto_arima(train, seasonal=True, m=12)
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# make your forecasts
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forecasts = model.predict(test.shape[0]) # predict N steps into the future
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# Visualize the forecasts (blue=train, green=forecasts)
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x = np.arange(y.shape[0])
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plt.plot(x[:150], train, c='blue')
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plt.plot(x[150:], forecasts, c='green')
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plt.show()
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```
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<img src="http://alkaline-ml.com/img/static/pmdarima_readme_example1.png" alt="Wineind example"/>
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Fitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset,
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serializing it, and then loading it from disk to make predictions:
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```python
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import pmdarima as pm
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from pmdarima.model_selection import train_test_split
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from pmdarima.pipeline import Pipeline
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from pmdarima.preprocessing import BoxCoxEndogTransformer
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import pickle
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# Load/split your data
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y = pm.datasets.load_sunspots()
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train, test = train_test_split(y, train_size=2700)
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# Define and fit your pipeline
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pipeline = Pipeline([
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('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)), # lmbda2 avoids negative values
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('arima', pm.AutoARIMA(seasonal=True, m=12,
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suppress_warnings=True,
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trace=True))
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])
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pipeline.fit(train)
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# Serialize your model just like you would in scikit:
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with open('model.pkl', 'wb') as pkl:
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pickle.dump(pipeline, pkl)
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# Load it and make predictions seamlessly:
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with open('model.pkl', 'rb') as pkl:
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mod = pickle.load(pkl)
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print(mod.predict(15))
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# [25.20580375 25.05573898 24.4263037 23.56766793 22.67463049 21.82231043
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# 21.04061069 20.33693017 19.70906027 19.1509862 18.6555793 18.21577243
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# 17.8250318 17.47750614 17.16803394]
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```
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### Availability
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`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.7+ for the following platforms:
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* Mac (64-bit)
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* Linux (64-bit manylinux)
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* Windows (64-bit)
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* 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10
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If a wheel doesn't exist for your platform, you can still `pip install` and it
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will build from the source distribution tarball, however you'll need `cython>=0.29`
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and `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source.
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Note that legacy versions (<1.0.0) are available under the name
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"`pyramid-arima`" and can be pip installed via:
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```bash
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# Legacy warning:
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$ pip install pyramid-arima
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# python -c 'import pyramid;'
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```
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However, this is not recommended.
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## Documentation
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All of your questions and more (including examples and guides) can be answered by
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the [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). If not, always
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feel free to file an issue.
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