Version 1.6.0

• Support newest versions of matplotlib
• Add new level of auto_arima error actions: "trace" which will warn for errors while dumping the original stacktrace.
• New featurizer: pmdarima.preprocessing.DateFeaturizer. This can be used to create dummy and ordinal exogenous features and is useful when modeling pseudo-seasonal trends or time series with holes in them.
• Removes first-party conda distributions (see #326)
• Raise a ValueError in arima.predict_in_sample when start < d

Version 1.6.0 Release Candidate

Release candidate for 1.6.0 release

Version 1.5.3

Version 1.5.3

• Adds first-party conda distributions as requested in #173
  • Due to dependency limitations, we only support 64-bit architectures and Python 3.6 or 3.7
• Adds Python 3.8 support as requested in #199
• Added pmdarima.datasets.load_gasoline
• Added integer levels of verbosity in the trace argument
• Added support for statsmodels 0.11+
• Added pmdarima.model_selection.cross_val_predict, as requested in #291

Version 1.5.2

Version 1.5.2

• Added pmdarima.show_versions as a utility for issue filing
• Fixed deprecation for check_is_fitted in newer versions of scikit-learn
• Adds the pmdarima.datasets.load_sunspots() method with R’s sunspots dataset
• Adds the pmdarima.model_selection.train_test_split() method
• Fix bug where 1.5.1 documentation was labeled version “0.0.0”.
• Fix bug reported in #271, where the use of threading.local to store stepwise context information may have broken job schedulers.
• Fix bug reported in #272, where the new default value of max_order can cause a ValueError even in default cases when stepwise=False.

Version 1.5.1

Fixes a bug in v1.5.0 where the pmdarima.__version__ attribute returned 0.0.0

Version 1.5.0

• No longer use statsmodels' ARIMA or ARMA class under the hood; only use
  the SARIMAX model, which cuts back on a lot of errors/warnings we saw in the past.
  (#211)

• Defaults in the ARIMA class that have changed as a result of #211:

  • maxiter is now 50 (was None)
  • method is now 'lbfgs' (was None)
  • seasonal_order is now (0, 0, 0, 0) (was None)
  • max_order is now 5 (was 10) and is no longer used as a constraint when stepwise=True

• Correct bug where aicc always added 1 (for constant) to degrees of freedom,
  even when df_model accounted for the constant term.

• New pmdarima.arima.auto.StepwiseContext feature for more control over
  fit duration (introduced by @kpsunkara in #221.

• Adds the pmdarima.preprocessing.LogEndogTransformer class as discussed in
  #205

• Exogenous arrays are no longer cast to numpy array by default, and will pass pandas
  frames through to the model. This keeps variable names intact in the summary #222

• Added the prefix param to exogenous featurizers to allow the addition of meaningful
  names to engineered features.

• Added polyroot test of near non-invertibility when stepwise=True. For
  models that are near non-invertible will be deprioritized in model selection
  as requested in #208

• Removes pmdarima.arima.ARIMA.add_new_samples, which was previously deprecated.
  Use pmdarima.arima.ARIMA.update instead.

• The following args have been deprecated from the pmdarima.arima.ARIMA class
  as well as pmdarima.arima.auto_arima and any other calling methods/classes:

  • disp^[1]
  • callback^[1]
  • transparams
  • solver
  • typ

  [1] These can still be passed to the fit method via **fit_kwargs, but should
  no longer be passed to the model constructor.

• Added diff_inv function that is in parity with R's implementation, as requested in #180

• Added decompose function that is in parity with R's implementation,
  as requested in #190

Version 1.4.0

• Fixes #191, an issue where the OCSB test could raise ValueError: negative dimensions are not allowed" in OCSB test

• Add option to automatically inverse-transform endogenous transformations when predicting
  from pipelines (#197)

• Add predict_in_sample to pipeline (#196)

• Parameterize dtype option in datasets module

• Adds the model_selection submodule, which defines several different cross-validation
  classes as well as CV functions:

  • pmdarima.model_selection.RollingForecastCV
  • pmdarima.model_selection.SlidingWindowForecastCV
  • pmdarima.model_selection.cross_validate
  • pmdarima.model_selection.cross_val_score

• Adds the pmdarima.datasets.load_taylor dataset

Version 1.3.0

v1.3.0

• 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

Version 1.2.1

This is a patch release specifically to get around the statsmodels issue:

statsmodels/statsmodels#5747

This pins scipy at 1.12 until statsmodels releases 0.10.0 (at some point in June 2019). Additionally, deprecation warnings are fixed in the scikit-learn dependency.

Version 1.2.0

v1.2.0

• 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:
  • PMDARIMA_CACHE and PYRAMID_ARIMA_CACHE
  • PMDARIMA_CACHE_WARN_SIZE and PYRAMID_ARIMA_CACHE_WARN_SIZE
  • PYRAMID_MPL_DEBUG
  • PYRAMID_MPL_BACKEND
• 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.