What’s new in pmdarima

As new releases of pmdarima are pushed out, the following list (introduced in v0.8.1) will document the latest features.


  • Adds a new dataset for stock prediction, along with an associated example (load_msft)
  • Fixes a bug in predict_in_sample, as addressed in #140.
  • Numpy 1.16+ is now required
  • Statsmodels 0.10.0+ is now required
  • Added sarimax_kwargs to ARIMA constructor and auto_arima function. This fixes #146


  • Pins scipy at 1.2.0 to avoid a statsmodels bug.


  • Adds the OCSBTest of seasonality, as discussed in #88
  • Default value of seasonal_test changes from “ch” to “ocsb” in auto_arima
  • Default value of test changes from “ch” to “ocsb” in nsdiffs
  • Adds benchmarking notebook and capabilities in pytest plugins
  • Removes the following environment variables, which are now deprecated:
  • Deprecates the is_stationary method in tests of stationarity. This will be removed in v1.4.0. Use should_diff instead.
  • Adds two new datasets: airpassengers & austres
  • When using out_of_sample, the out-of-sample predictions are now stored under the oob_preds_ attribute.
  • Adds a number of transformer classes including:
    • BoxCoxEndogTransformer
    • FourierFeaturizer
  • Adds a Pipeline class resembling that of scikit-learn’s, which allows the stacking of transformers together.
  • Adds a class wrapper for auto_arima: AutoARIMA. This is allows auto-ARIMA to be used with pipelines.


v1.1.1 is a patch release in response to #104

  • Deprecates the ARIMA.add_new_observations method. This method originally was designed to support updating the endogenous/exogenous arrays with new observations without changing the model parameters, but achieving this behavior for each of statsmodels’ ARMA, ARIMA and SARIMAX classes proved nearly impossible, given the extremely complex internals of statmodels estimators.
  • Replaces ARIMA.add_new_observations with ARIMA.update. This allows the user to update the model with new observations by taking maxiter new steps from the existing model coefficients and allowing the MLE to converge to an updated set of model parameters.
  • Changes default maxiter to None, using 50 for seasonal models and 500 for non-seasonal models (as statsmodels does). The default value used to be 50 for all models.
  • New behavior in ARIMA.fit allows start_params and maxiter to be passed as **fit_args, overriding the use of their corresponding instance attributes.


  • Adds ARIMA.plot_diagnostics method, as requested in #49
  • Adds new arg to ARIMA constructor and auto_arima: with_intercept (default is True).
  • New default for trend is no longer 'c', it is None.
  • Adds to_dict method to ARIMA class to address Issue #54
  • ARIMA serialization no longer stores statsmodels results wrappers in the cache, but bundles them into the pickle file. This solves Issue #48 and only works on statsmodels 0.9.0+ since they’ve fixed a bug on their end.
  • The 'PMDARIMA_CACHE' and 'PMDARIMA_CACHE_WARN_SIZE' environment variables are now deprecated, since they no longer need to be used.
  • Added versioned documentation. All releases’ doc (from 0.9.0 onward) is now available at alkaline-ml.com/pmdarima/<version>
  • Fixes bug in ADFTest where OLS was computed with method="pinv" rather than "method=qr". This fix means better parity with R’s results. See #71 for more context.
  • CHTest now solves linear regression with normalize=True. This solves #74
  • Python 3.7 is now supported(!!)


  • Wheels are no longer built for Python versions < 3.5. You may still be able to build from source, but support for 2.x python versions will diminish in future versions.
  • Migrates namespace from ‘pyramid-arima’ to ‘pmdarima’. This is due to the fact that a growing web-framework (also named Pyramid) is causing namespace collisions when both packages are installed on a machine. See Issue #34 for more detail.
  • Removes redundant Travis tests
  • Automates documentation build on Circle CI
  • Moves lots of the build/test functionality into the Makefile for ease.
  • Warns for impending deprecation of various environment variable name changes. The following will be completely switched over in version 1.2.0:


  • Explicitly catches case in auto_arima where a value of m that is too large may over-estimate D, causing the time series to be differenced down to an empty array. This is now handled by raising a separate error for this case that better explains what happened.
  • Re-pickling an ARIMA will no longer remove the location on disk of the cached statsmodels ARIMA models. Older versions encountered an issue where an older version of the model would be reinstated and immediately fail due to an OSError since the cached state no longer existed. This means that a user must be very intentional about clearing out the pyramid cache over time.
  • Adds pyramid cache check on initial import to warn user if the cache size has grown too large.
  • If d or D are explicitly defined for auto_arima (rather than None), do not raise an error if they exceed max_d or max_D, respectively.
  • Adds Circle CI for validating PyPy builds (rather than CPython)
  • Deploys python wheel for version 3.6 on Linux and Windows
  • Includes warning for upcoming package name change (pmdarima).


  • New ARIMA instance attributes

    • The pkg_version_ attribute (assigned on model fit) is new as of version 0.8.0. On unpickling, if the current Pyramid version does not match the version under which it was serialized, a UserWarning will be raised.
  • Addition of the _config.py file at the top-level of the package

  • Fixes bug (Issue #30) in ARIMA where using CV with differencing and no seasonality caused a dim mismatch in the model’s exog array and its endog array

  • New dataset: Woolyrnq (from R’s forecast package).

  • Visualization utilities available at the top level of the package:

    • plot_acf
    • plot_pacf
    • autocorr_plot
  • Updates documentation with significantly more examples and API references.


  • out_of_sample_size behavior in pmdarima.arima.ARIMA
    • In prior versions, the out_of_sample_size (OOSS) parameter misbehaved in the sense that it ended up fitting the model on the entire sample, and scoring the number specified. This behavior changed in v0.7.0. Going forward, when OOSS is not None, ARIMA models will be fit on \(n - OOSS\) samples, scored on the last OOSS samples, and the held-out samples are then added to the model.
  • Adds add_new_samples method to pmdarima.arima.ARIMA
    • This method adds new samples to the model, effectively refreshing the point from which it creates new forecasts without impacting the model parameters.
  • Adds confidence intervals on predict in pmdarima.arima.ARIMA
    • When return_conf_int is true, the confidence intervals will now be returned with the forecasts.


  • pmdarima.arima.CHTest of seasonality
    • No longer computes the \(U\) or \(V\) matrix in the SVD computation in the Canova-Hansen test. This makes the test much faster.