.. _quickstart: ========== Quickstart ========== Since pmdarima is intended to replace R's ``auto.arima``, the interface is designed to be quick to learn and easy to use, even for R users making the switch. Common functions and tools are elevated to the top-level of the package: .. code-block:: python import pmdarima as pm # Create an array like you would in R x = pm.c(1, 2, 3, 4, 5, 6, 7) # Compute an auto-correlation like you would in R: pm.acf(x) # Plot an auto-correlation: pm.plot_acf(x) Auto-ARIMA example ------------------ Here's a quick example of how we can fit an ``auto_arima`` with pmdarima: .. code-block:: python import numpy as np import pmdarima as pm from pmdarima.datasets import load_wineind # this is a dataset from R wineind = load_wineind().astype(np.float64) # fit stepwise auto-ARIMA stepwise_fit = pm.auto_arima(wineind, start_p=1, start_q=1, max_p=3, max_q=3, m=12, start_P=0, seasonal=True, d=1, D=1, trace=True, error_action='ignore', # don't want to know if an order does not work suppress_warnings=True, # don't want convergence warnings stepwise=True) # set to stepwise It's easy to examine your model fit results. Simply use the ``summary``:sup:`[1]` method: .. code-block:: python >>> stepwise_fit.summary() """ SARIMAX Results ============================================================================================ Dep. Variable: y No. Observations: 176 Model: SARIMAX(0, 1, 2)x(0, 1, [1], 12) Log Likelihood -1528.766 Date: Wed, 15 Jun 2022 AIC 3065.533 Time: 12:38:14 BIC 3077.908 Sample: 0 HQIC 3070.557 - 176 Covariance Type: opg ============================================================================== coef std err z P>|z| [0.025 0.975] ------------------------------------------------------------------------------ ma.L1 -0.5756 0.041 -13.952 0.000 -0.656 -0.495 ma.L2 -0.1065 0.048 -2.224 0.026 -0.200 -0.013 ma.S.L12 -0.3848 0.054 -7.156 0.000 -0.490 -0.279 sigma2 7.866e+06 7.01e+05 11.228 0.000 6.49e+06 9.24e+06 =================================================================================== Ljung-Box (L1) (Q): 2.84 Jarque-Bera (JB): 18.05 Prob(Q): 0.09 Prob(JB): 0.00 Heteroskedasticity (H): 1.17 Skew: -0.55 Prob(H) (two-sided): 0.56 Kurtosis: 4.21 =================================================================================== Warnings: [1] Covariance matrix calculated using the outer product of gradients (complex-step). [1] The summary output was generated using the following versions: .. code-block:: python >>> import pmdarima as pm >>> pm.show_versions() System: python: 3.9.7 (default, Nov 10 2021, 08:50:17) [Clang 13.0.0 (clang-1300.0.29.3)] executable: /Users/asmith/venv/bin/python machine: macOS-11.6.6-x86_64-i386-64bit Python dependencies: pip: 21.2.3 setuptools: 57.4.0 sklearn: 1.1.1 statsmodels: 0.13.2 numpy: 1.22.4 scipy: 1.8.1 Cython: 0.29.30 pandas: 1.4.2 joblib: 1.1.0 pmdarima: 1.8.5