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NeoProphet 1.4.0

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@CodeWithBehnam CodeWithBehnam released this 10 Oct 20:50
· 11 commits to main since this release
v1.4.0
5d0b105

First release of NeoProphet on PyPI: a drop-in fork of Facebook Prophet that is easier to use, clearer to read and faster at scale.

pip install neoprophet            # or: uv add neoprophet
pip install "neoprophet[mlx]"     # adds the MLX backend on Apple Silicon

NeoProphet installs the prophet import package, so existing code keeps working. Uninstall upstream prophet first and do not install both in one environment.

Highlights

  • One-call forecasting: forecast(df, horizon="12 months") accepts any date and value columns or a Series with a DatetimeIndex, detects the frequency and explains bad input with specific errors.
  • Charts that explain themselves: a new default style for every matplotlib and plotly chart, with data-derived plain-English titles, history and forecast zones and changepoint callouts. Switch the extras off with title=False, annotate=False.
  • Built-in tuning: forecast(..., tune=True) or prophet.tune(df) chooses the prior scales and seasonality mode by parallel cross-validation and returns ranked scores.
  • Many series on Apple Silicon (experimental): prophet.mlx_backend.fit_many(dfs) fits 100 series in 1.3 s, 25x faster than fitting them one by one with upstream Prophet, with forecasts within 0.6% of the data range of the CmdStan fits. Linear and flat growth, MAP only.
  • Faster predictions: predict with uncertainty is about 17% faster and cross-validation 13 to 22% faster, with bit-for-bit identical results for a fixed seed.

Fixes

  • plot_plotly no longer crashes on assert m.history, which tested a DataFrame for truthiness.
  • macOS wheels link with header padding so the bundled TBB library can be relocated.

Benchmarks against upstream Prophet 1.5.0

On the 8 example datasets, default NeoProphet forecasts are identical to upstream. Tuning beat untuned Prophet defaults on 4 datasets and was worse on 2, and hand-chosen settings still won on most, so validate tuned models on a holdout. Full results are in notebooks/benchmark_vs_upstream.ipynb.

Prophet was created by Sean J. Taylor and Ben Letham at Facebook. NeoProphet is released under the MIT licence.

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