What's different

Effect Prophet fits the same model as Python Prophet 1.4.0 (see How close are the results?). This page lists where the library does less than Python, where it differs on purpose, and the smaller behavior differences.

Not available yet

• Full Bayesian fitting (mcmc_samples)
• Warm starts (warm_start_params)
• Forecasting regressors with their own models. Every future row must supply its regressor values.
• Monthly horizons and custom metrics in cross-validation

Different on purpose

• Plain data and typed errors instead of a mutable Prophet object and pandas dataframes. Rows are { timestamp, value } instead of ds and y.
• Strict UTC timestamps; no guessing at formats or time zones
• Missing values are rejected rather than silently dropped
• You supply future dates; there is no make_future_dataframe
• No built-in plotting
• Saved models use their own format, not Python's JSON
• Country calendars are offline Prophet 1.4.0 / holidays 0.104 snapshots with an outer 1995–2060 horizon. Known missing coverage is rejected unless explicitly accepted with coverage: "allow-known-gaps"; neither policy guarantees completeness. Only country-level defaults are supported, not subdivisions/languages/categories. Saved models embed their selected snapshot and never silently adopt installed updates. See Country holiday calendars.
• Cross-validation and model search run one fold after another, with no worker parallelism
• Uncertainty ranges are a separate predictUncertainty call, so point forecasts never pay for simulations. They need a seed, so results are repeatable.

Small behavior differences

• Uncertainty simulation. Ranges use Prophet's per-path simulation (vectorized=False), not Python's default shortcut. The shortcut only allows trend changes at the requested rows and assumes they are evenly spaced, but here you supply your own rows. On evenly spaced rows the two agree: in our long-history benchmark, the average trend width of future ranges was 17.7 for the shortcut and 17.8 for the per-path method over eight seeds.
• Size limits on uncertainty. One call takes up to 10,000 rows and 2,048 samples. Python has no such limits. See Uncertainty ranges for details.
• Memory limit for Newton's method. Newton's method, used for histories under 100 rows and as L-BFGS's fallback, keeps four tables of size parameters × parameters. The engine can use at most 4 GiB of memory, so Newton can never fit more than 11,585 parameters. In practice the limit is somewhat lower, because the rest of the fit needs memory too, and lower still if your runtime gives the engine less. Each Fourier order adds two parameters, and changepoints, events and regressors add more. Past the limit, fit fails with a FittingError whose reason is "memory-limit", with parameterCount and memoryLimitBytes. Python has no such limit.
• Inputs that cannot be fitted return an error. If every row has the same timestamp, there is no way to draw a trend, so fit fails where Python returns NaN forecasts. These are the intentional differences in the benchmarks.
• Last-digit differences. Fits stop when they are close enough, and rounding differs across computers, so forecasts agree to many decimal places but not always all of them. For logistic growth, the fitted internal numbers can differ slightly even when forecasts match; the benchmarks flag these cases.