Prophet

What's Changed

Python

- 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.

R

- Updated holidays data based on holidays v0.34

**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](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#modeling-holidays-and-special-events).
* 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.

v1.1.3-patched
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](https://github.com/facebook/prophet/pull/2186). Credits to orenmatar for the original [blog post](https://github.com/facebook/prophet/issues/2030) 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](https://mc-stan.org/docs/2_29/functions-reference/normal-id-glm.html) 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