1.1.6 (PyPI publishing fix)

See previous release for actual changelog.

1.1.6

What's Changed

• Update plot.py by @tomthepeach in #2523
• Bug Fix: Include predictions for missing y (NaN) dates in the history #2518 by @imad24 in #2530
• Use stable sort when sorting values in forecaster by @natl in #2568
• Fix Issue #2577 - Zero Division Error in diagnostics.performance_metrics() causing failed assertion by @ThomasChia in #2578
• Fixed np.float_ deprecation in numpy 2.0 by @shamalgithub in #2592
• Removed remaining depreciated datetime conversions (#2492) by @YuyuZhou1228 in #2598
• Update holidays package dependency constraints by @arkid15r in #2587

New Contributors

• @tomthepeach made their first contribution in #2523
• @imad24 made their first contribution in #2530
• @natl made their first contribution in #2568
• @ThomasChia made their first contribution in #2578
• @shamalgithub made their first contribution in #2592
• @YuyuZhou1228 made their first contribution in #2598

Full Changelog: 1.1.5...v1.1.6

1.1.5

What's Changed

Python

• Upgraded cmdstan version to 2.33.1, enabling Apple M2 support. @lucentcosmos @tcuongd
• Added a binary distribution for the macOS arm64 architecture (M1, M2 chips). @tcuongd
• Added argument scaling to the Prophet() instantiation. Allows minmax scaling on y instead of
  absmax scaling (dividing by the maximum value). scaling='absmax' by default, preserving the
  behaviour of previous versions. @yoziru
• Added argument holidays_mode to the Prophet() instantiation. Allows holidays regressors to have
  a different mode than seasonality regressors. holidays_mode takes the same value as seasonality_mode
  if not specified, preserving the behaviour of previous versions. @CoreyBryant-everi
• Added two methods to the Prophet object: preprocess() and calculate_initial_params(). These
  do not need to be called and will not change the model fitting process. Their purpose is to provide
  clarity on the pre-processing steps taken (y scaling, creating fourier series, regressor scaling,
  setting changepoints, etc.) before the data is passed to the stan model. @tcuongd
• Added argument extra_output_columns to cross_validation(). The user can specify additional columns
  from predict() to include in the final output alongside ds and yhat, for example extra_output_columns=['trend']. @dchiang00
• prophet's custom hdays module was deprecated last version and is now removed.

R

• Updated holidays data based on holidays v0.34

Full Changelog: v1.1.4...1.1.5

1.1.4

What's Changed

Python

• We now rely solely on the holidays package for country holidays. Credits to @arkid15r in #2379
  • This allows us to take full advantage of improvements to the holidays package, and removes reliance on unmaintained manual holidays entries in hdays.py. Importing from the prophet.hdays module has been deprecated and the module will be removed in the next release.
  • holidays v0.20 and beyond contains most / all previously missing holidays, so there should be very little differences in fitted models and predictions that currently make use of inbuilt country holidays.
  • Note that for countries like India and Pakistan, some Christian holidays are currently not included in the holidays package so will not be added automatically with .add_country_holidays(). These can be added manually instead, see examples here.
• Upgraded underlying cmdstan to v2.31.0, which fixes installation issues on Apple M1. Credits to @WardBrian in #2428
• Fixed a bug with Windows wheel builds caused by long path names. Credits to @WardBrian

R

• Updated holidays data based on holidays v0.25.

v1.1.3-patched

What's Changed

• Ensure compatibility with holidays 0.25.0 (#2431). Credits to @arkid15r

Full Changelog: v1.1.2...v1.1.3-patched

1.1.2

What's Changed

Python

• Sped up .predict() by up to 10x by removing intermediate DataFrame creations. @orenmatar (#2299)
• Sped up fourier series generation, leading to at least 1.5x speed improvement for train() and predict() pipelines. @yoziru (#2334)
• Fixed bug in how warm start values were being read. Documentation has been updated. @tcuongd
• Developer experience: modernized build / develop / test workflow based on https://discuss.python.org/t/custom-build-steps-moving-bokeh-off-setup-py/16128/17 @tcuongd
• Wheels are now version-agnostic. This is possible because we don't use any Python-version-specific tooling to compile stan models. Credits to @WardBrian for the suggestion and this example: https://github.com/joerick/python-ctypes-package-sample.

R

• Fixed a bug in construct_holiday_dataframe()
• Updated holidays data based on holidays version 0.18.
• Note that the 1.1.2 has been submitted to CRAN but is not live yet. You can install by downloading the attached files: .tar.gz to install from source, or .tgz for the macOS binary.

1.1.1

What's Changed

Python

• Improved runtime of predict() function via vectorization of future draws. Details here. Credits to @orenmatar for the original blog post and @winedarksea @tcuongd for the implementation.
  • predict() now has a new argument, vectorized, which is true by default. You should see speedups of 3-7x for predictions, especially if the model does not use full MCMC sampling. When using growth='logistic' with mcmc_samples > 0, predictions may be slower, and in these cases you can fall back to the original code by specifying vectorized=False.
• Added aarch64 wheels for Linux and parallelised wheel build workflow. @thechopkins
• cmdstanpy minimum version is now 1.0.4.
• Fixed a bug where the version number hadn't updated from 1.0. prophet.__version__ now returns the correct version. @tcuongd

R

• (Backend change) Make holidays data internal to the package to prevent unintentional overrides. Note that the data can still be read by end users as before, this hasn't changed. @bartekch

1.1

What's Changed

Python

• Minimum required version of Python is now 3.7
• Removed dependency on pystan==2.19.1.1, which is no longer maintained. cmdstanpy is now the sole stan backend. @tcuongd @WardBrian @akosfurton @malmashhadani-88
• Python binaries built for MacOS, Linux, and Windows for Python 3.7-3.10, so end users no longer need to compile the Prophet model from source on their machine. @tcuongd
  • Binaries are built using Github Actions. @tcuongd @abitrolly
• Use normal-id-glm distribution for Stan model to improve MCMC sampling speed (the model itself is still identical). @andrjohns
• Improved execution time of rolling_mean_by_h function used to calculate cross validation performance metrics. @RaymondMcT

R

• Update holidays data based on holidays package version 0.13.

v1.0

• Python package name changed from fbprophet to prophet
• Bugfixes in R timezone handling, serialization, and holidays
• Fixed R Windows build issues to get latest version back on CRAN
• Improvements in plots

v0.7

• Built-in json serialization
• Added "flat" growth option
• Bugfixes related to holidays and pandas
• Plotting improvements
• Improvements in cross validation, such as parallelization and directly specifying cutoffs