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(Deep Learning based) Time Series Analysis\u003c/span\u003e","Time Series","Time-Series \u0026 Financial","📦 Packages","时间序列","Python：量化金融第一生态","⏳ Time Series Analysis","Time Series Analysis \u0026 Econometrics"],"sub_categories":["预测","Cryptocurrencies","2020","TimeSeries Analysis","Others","Time Series","Python","六、时间序列分析与预测","Tools"],"readme":"# pmdarima\n\n[![PyPI version](https://badge.fury.io/py/pmdarima.svg)](https://badge.fury.io/py/pmdarima)\n[![CircleCI](https://circleci.com/gh/alkaline-ml/pmdarima.svg?style=svg)](https://circleci.com/gh/alkaline-ml/pmdarima)\n[![Mac and Windows Builds](https://github.com/alkaline-ml/pmdarima/actions/workflows/build_and_deploy.yml/badge.svg)](https://github.com/alkaline-ml/pmdarima/actions/workflows/build_and_deploy.yml)\n[![codecov](https://codecov.io/gh/alkaline-ml/pmdarima/branch/master/graph/badge.svg)](https://codecov.io/gh/alkaline-ml/pmdarima)\n![Supported versions](https://img.shields.io/badge/python-3.10+-blue.svg)\n![Downloads](https://img.shields.io/badge/dynamic/json?color=blue\u0026label=downloads\u0026query=%24.total\u0026url=https%3A%2F%2Fstore.zapier.com%2Fapi%2Frecords%3Fsecret%3D1e061b29db6c4f15af01103d403b0237)\n![Downloads/Week](https://img.shields.io/badge/dynamic/json?color=blue\u0026label=downloads%2Fweek\u0026query=%24.weekly\u0026url=https%3A%2F%2Fstore.zapier.com%2Fapi%2Frecords%3Fsecret%3D1e061b29db6c4f15af01103d403b0237)\n\nPmdarima (originally `pyramid-arima`, for the anagram of 'py' + 'arima') is a statistical\nlibrary designed to fill the void in Python's time series analysis capabilities. This includes:\n\n  * The equivalent of R's [`auto.arima`](https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima) functionality\n  * A collection of statistical tests of stationarity and seasonality\n  * Time series utilities, such as differencing and inverse differencing\n  * Numerous endogenous and exogenous transformers and featurizers, including Box-Cox and Fourier transformations\n  * Seasonal time series decompositions\n  * Cross-validation utilities\n  * A rich collection of built-in time series datasets for prototyping and examples\n  * Scikit-learn-esque pipelines to consolidate your estimators and promote productionization\n  \nPmdarima wraps [statsmodels](https://github.com/statsmodels/statsmodels/blob/master/statsmodels)\nunder the hood, but is designed with an interface that's familiar to users coming\nfrom a scikit-learn background.\n\n## Installation\n\n### pip\n\nPmdarima has binary and source distributions for Windows, Mac and Linux (`manylinux`) on pypi\nunder the package name `pmdarima` and can be downloaded via `pip`:\n\n```bash\npip install pmdarima\n```\n\n### conda\n\nPmdarima also has Mac and Linux builds available via `conda` and can be installed like so:\n\n```bash\nconda config --add channels conda-forge\nconda config --set channel_priority strict\nconda install pmdarima\n```\n\n**Note:** We do not maintain our own Conda binaries, they are maintained at https://github.com/conda-forge/pmdarima-feedstock.\nSee that repo for further documentation on working with Pmdarima on Conda.\n\n## Quickstart Examples\n\nFitting a simple auto-ARIMA on the [`wineind`](https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.datasets.load_wineind.html#pmdarima.datasets.load_wineind) dataset:\n\n```python\nimport pmdarima as pm\nfrom pmdarima.model_selection import train_test_split\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load/split your data\ny = pm.datasets.load_wineind()\ntrain, test = train_test_split(y, train_size=150)\n\n# Fit your model\nmodel = pm.auto_arima(train, seasonal=True, m=12)\n\n# make your forecasts\nforecasts = model.predict(test.shape[0])  # predict N steps into the future\n\n# Visualize the forecasts (blue=train, green=forecasts)\nx = np.arange(y.shape[0])\nplt.plot(x[:150], train, c='blue')\nplt.plot(x[150:], forecasts, c='green')\nplt.show()\n```\n\n\u003cimg src=\"http://alkaline-ml.com/img/static/pmdarima_readme_example1.png\" alt=\"Wineind example\"/\u003e\n\n\nFitting a more complex pipeline on the [`sunspots`](https://www.rdocumentation.org/packages/datasets/versions/3.6.1/topics/sunspots) dataset,\nserializing it, and then loading it from disk to make predictions:\n\n```python\nimport pmdarima as pm\nfrom pmdarima.model_selection import train_test_split\nfrom pmdarima.pipeline import Pipeline\nfrom pmdarima.preprocessing import BoxCoxEndogTransformer\nimport pickle\n\n# Load/split your data\ny = pm.datasets.load_sunspots()\ntrain, test = train_test_split(y, train_size=2700)\n\n# Define and fit your pipeline\npipeline = Pipeline([\n    ('boxcox', BoxCoxEndogTransformer(lmbda2=1e-6)),  # lmbda2 avoids negative values\n    ('arima', pm.AutoARIMA(seasonal=True, m=12,\n                           suppress_warnings=True,\n                           trace=True))\n])\n\npipeline.fit(train)\n\n# Serialize your model just like you would in scikit:\nwith open('model.pkl', 'wb') as pkl:\n    pickle.dump(pipeline, pkl)\n    \n# Load it and make predictions seamlessly:\nwith open('model.pkl', 'rb') as pkl:\n    mod = pickle.load(pkl)\n    print(mod.predict(15))\n# [25.20580375 25.05573898 24.4263037  23.56766793 22.67463049 21.82231043\n# 21.04061069 20.33693017 19.70906027 19.1509862  18.6555793  18.21577243\n# 17.8250318  17.47750614 17.16803394]\n```\n\n\n### Availability\n\n`pmdarima` is available on PyPi in pre-built Wheel files for Python 3.10+ for the following platforms:\n\n* Mac (64-bit)\n* Linux (64-bit manylinux)\n* Windows (64-bit)\n  * 32-bit wheels are available for pmdarima versions below 2.0.0 and Python versions below 3.10\n\nIf a wheel doesn't exist for your platform, you can still `pip install` and it\nwill build from the source distribution tarball, however you'll need `cython\u003e=0.29`\nand `gcc` (Mac/Linux) or `MinGW` (Windows) in order to build the package from source.\n\nNote that legacy versions (\u003c1.0.0) are available under the name\n\"`pyramid-arima`\" and can be pip installed via:\n\n```bash\n# Legacy warning:\n$ pip install pyramid-arima\n# python -c 'import pyramid;'\n```\n\nHowever, this is not recommended.\n\n## Documentation\n\nAll of your questions and more (including examples and guides) can be answered by\nthe [`pmdarima` documentation](https://www.alkaline-ml.com/pmdarima). 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