From df4fb4db3044f9269f54bd26293d2da926990347 Mon Sep 17 00:00:00 2001 From: Aaron Smith Date: Wed, 15 Jun 2022 15:22:21 -0500 Subject: [PATCH] Update doc and add version footnote (#509) --- doc/quickstart.rst | 64 ++++++++++++++++++++++++++++++---------------- 1 file changed, 42 insertions(+), 22 deletions(-) diff --git a/doc/quickstart.rst b/doc/quickstart.rst index 7882553d7..6998d8f1f 100644 --- a/doc/quickstart.rst +++ b/doc/quickstart.rst @@ -45,39 +45,59 @@ Here's a quick example of how we can fit an ``auto_arima`` with pmdarima: 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`` method: +It's easy to examine your model fit results. Simply use the ``summary``:sup:`[1]` method: .. code-block:: python >>> stepwise_fit.summary() """ - Statespace Model Results - ========================================================================================== - Dep. Variable: y No. Observations: 176 - Model: SARIMAX(1, 1, 2)x(0, 1, 1, 12) Log Likelihood -1527.386 - Date: Mon, 04 Sep 2017 AIC 3066.771 - Time: 13:59:01 BIC 3085.794 - Sample: 0 HQIC 3074.487 - - 176 - Covariance Type: opg + 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] ------------------------------------------------------------------------------ - intercept -100.7446 72.306 -1.393 0.164 -242.462 40.973 - ar.L1 -0.5139 0.390 -1.319 0.187 -1.278 0.250 - ma.L1 -0.0791 0.403 -0.196 0.844 -0.869 0.710 - ma.L2 -0.4438 0.223 -1.988 0.047 -0.881 -0.006 - ma.S.L12 -0.4021 0.054 -7.448 0.000 -0.508 -0.296 - sigma2 7.663e+06 7.3e+05 10.500 0.000 6.23e+06 9.09e+06 + 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 (Q): 48.66 Jarque-Bera (JB): 21.62 - Prob(Q): 0.16 Prob(JB): 0.00 - Heteroskedasticity (H): 1.18 Skew: -0.61 - Prob(H) (two-sided): 0.54 Kurtosis: 4.31 + 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). - [2] Covariance matrix is singular or near-singular, with condition number 8.15e+14. Standard errors may be unstable. - """ + +[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