prophet

What's Changed

• Upgraded cmdstan version to 2.33.1, enabling Apple M2 support.
• Added pre-built wheels for macOS arm64 architecture (M1, M2 chips).
• 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. Credits to @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. Credits to @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.
• 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']. Credits to @dchiang00
• prophet's custom hdays module was deprecated last version and is now removed.

Full Changelog: https://github.com/facebook/prophet/compare/v1.1.4...1.1.5

What's Changed

Python

• We now rely solely on the holidays package for country holidays. Credits to @arkid15r in https://github.com/facebook/prophet/pull/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 https://github.com/facebook/prophet/pull/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.

What's Changed

• Ensure compatibility with holidays 0.25.0 (https://github.com/facebook/prophet/pull/2431). Credits to @arkid15r

Full Changelog: https://github.com/facebook/prophet/compare/v1.1.2...v1.1.3-patched

What's Changed

Python

• Sped up .predict() by up to 10x by removing intermediate DataFrame creations. Credits to @orenmatar (https://github.com/facebook/prophet/pull/2299)
• Sped up fourier series generation, leading to at least 1.5x speed improvement for train() and predict() pipelines. Credits to @yoziru (https://github.com/facebook/prophet/pull/2334)
• Fixed bug in how warm start values were being read. Documentation has been updated.
• Developer experience: modernized build / develop / test workflow based on https://discuss.python.org/t/custom-build-steps-moving-bokeh-off-setup-py/16128/17
• 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.

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 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. Credits to @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.

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. Credits to @bartekch.

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. Credits to @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.
  • Binaries are built using Github Actions. Credits to @abitrolly for simplifying the workflow.

Other improvements

• Use normal-id-glm distribution for Stan model to improve MCMC sampling speed (the model itself is still identical). Credits to @andrjohns
• Improved execution time of rolling_mean_by_h function used to calculate cross validation performance metrics. Credits to @RaymondMcT
• Update holidays data based on holidays package version 0.13.

• 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

• 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

• Fix bugs related to upstream changes in holidays and pandas packages.
• Compile model during first use, not during install (to comply with CRAN policy)
• cmdstanpy backend now available in Python

• Conditional seasonalities
• Improved cross validation estimates
• Plotly plot in python
• Bugfixes

• Added built-in holidays
• Bugfixes

• Multiplicative seasonality
• Cross validation error metrics and visualizations
• Parameter to set range of potential changepoints
• Unified Stan model for both trend types
• Improved future trend uncertainty for sub-daily data
• Bugfixes

• Bugfixes

• Forecasting with sub-daily data
• Daily seasonality, and custom seasonalities
• Extra regressors
• Access to posterior predictive samples
• Cross-validation function
• Saturating minimums
• Bugfixes

• numerous bugfixes
• automatically detect yearly and weekly seasonality (now the default)

Release of version 0.1