Seasonality, events and regressors benchmarks

35 of 35 cases match Python Prophet.

Custom and switchable seasonal patterns, events with windows, extra regressors, and additive and multiplicative effects together, on every trend type.

Each case runs the same input through Effect Prophet and Python Prophet 1.4.0, then compares every forecast row, the trend, each component and the fitted noise. A case matches when all of them agree within its tolerances, and only matching cases are timed.

Each case's dot shows its result: matches, matches with internal differences flagged, intentional difference, doesn't match. Hover over a dot for details.

How to read the tables:

• Effect output and Python output are each library's final forecast value. Open a case to see every row side by side.
• Largest difference is the biggest gap between the two forecasts, as a share of the input data's range.
• Effect time and Python time are the median time for the timed step, usually fitting and predicting. Open a case for every step, including first-run and save/load times.
• Flagged cases match, but the two libraries stopped their fitting search at slightly different points. The case page shows the internal numbers.

About these runs

Each trend's cases come from that trend's own run.

Linear growth

• Run 2026-10-20T170110-379Z-27b7ec98 on 2026-10-20, commit 27b7ec9
• Both libraries ran in linux/arm64 containers on Apple M4 Pro
• effect-prophet 0.0.0
• effect 4.0.0
• prophet 1.4.0
• Full benchmark report

Flat growth

• Run 2026-10-20T171807-628Z-27b7ec98 on 2026-10-20, commit 27b7ec9
• Both libraries ran in linux/arm64 containers on Apple M4 Pro
• effect-prophet 0.0.0
• effect 4.0.0
• prophet 1.4.0
• Full benchmark report

Logistic growth

• Run 2026-10-20T174321-690Z-27b7ec98 on 2026-10-20, commit 27b7ec9
• Both libraries ran in linux/arm64 containers on Apple M4 Pro
• effect-prophet 0.0.0
• effect 4.0.0
• prophet 1.4.0
• Full benchmark report

Timings come from one machine and describe this workload only. They are not a general speed ranking. Where Python is faster, it is often because Python runs Stan's optimizer as compiled C++, while Effect Prophet runs the same algorithm as WebAssembly with narrower vector instructions. The gap is widest for Newton's method on short histories with many parameters.

Linear growth

Custom and switchable seasonal patterns, events with windows, extra regressors, and additive and multiplicative effects mixed together. All linear growth benchmarks →

Case	Input	Effect output	Python output	Largest difference	Timed step	Effect time	Python time
Weekly pattern on irregular dates	192 rows	108.5119	108.5118	< 0.0001%	Fit and predict	1.49 ms	11.3 ms
Two switchable weekly patterns	256 rows	139.4380	139.4353	0.0028%	Fit and predict	3.11 ms	15.7 ms
Events with windows	96 rows	54.2821	54.2821	< 0.0001%	Fit and predict	6.77 ms	33.1 ms
Four regressors	256 rows	138.7457	138.7426	0.0032%	Fit and predict	2.96 ms	13.6 ms
Everything at once, small	24 rows	21.2025	21.2025	< 0.0001%	Fit and predict	1.65 ms	16.6 ms
Everything at once, two years	768 rows	54.2706	54.2657	0.015%	Fit and predict	12.9 ms	43.1 ms
Additive and multiplicative together	96 rows	44.5183	44.5183	< 0.0001%	Fit and predict	4.91 ms	31.8 ms
Repeated timestamps with features	96 rows	18.2191	18.2200	0.017%	Fit	32.6 ms	112 ms

Flat growth

Weekly patterns, events and regressors on a flat trend: with a switchable multiplicative weekly pattern at full length and cut to the history lengths cross-validation uses, with every effect additive, and with regressors that move almost or exactly in step with the level or the yearly pattern. All flat growth benchmarks →

Case	Input	Effect output	Python output	Largest difference	Timed step	Effect time	Python time
Weekly pattern, absmax scaling	96 rows	35.0039	35.0039	< 0.0001%	Fit	155 ms	603 ms
Weekly pattern, minmax scaling	96 rows	35.0039	35.0039	< 0.0001%	Fit	4.23 ms	20.4 ms
Mixed effects, minmax scaling	96 rows	35.0283	35.0283	< 0.0001%	Fit and predict	1.41 ms	12.4 ms
Mixed effects, absmax scaling	96 rows	35.0127	35.0127	< 0.0001%	Fit	1.58 ms	7.92 ms
Mixed effects, eight months, minmax scaling	256 rows	19.5305	19.5305	< 0.0001%	Fit and predict	2.23 ms	13.7 ms
Additive effects, minmax scaling	96 rows	35.0127	35.0127	< 0.0001%	Fit and predict	4.67 ms	26.9 ms
Additive effects, eight months, minmax scaling	256 rows	28.9196	28.9196	< 0.0001%	Fit and predict	2.12 ms	13.3 ms
Mixed effects, both libraries on default settings	96 rows	35.0012	35.0127	0.073%	Fit and predict	3.17 ms	48.4 ms
Regressors that track other effects	730 rows	56.9753	56.9753	< 0.0001%	Fit and predict	5.42 ms	29.6 ms
Mixed effects, eight months, absmax scaling	256 rows	28.9196	28.9196	< 0.0001%	Fit	3.82 ms	8.99 ms
First 63 days, absmax scaling	63 rows	24.0826	24.0826	< 0.0001%	Fit	2.24 ms	7.62 ms
First 63 days, minmax scaling	63 rows	7.1798	7.1798	< 0.0001%	Fit	2.21 ms	7.61 ms
First 70 days, absmax scaling	70 rows	24.0795	24.0795	< 0.0001%	Fit	2.10 ms	7.44 ms
First 70 days, minmax scaling	70 rows	7.1718	7.1718	< 0.0001%	Fit	2.04 ms	7.85 ms
First 77 days, absmax scaling	77 rows	24.0746	24.0746	< 0.0001%	Fit	2.52 ms	7.51 ms
First 77 days, minmax scaling	77 rows	7.1594	7.1594	< 0.0001%	Fit	2.16 ms	8.16 ms
First 225 days, absmax scaling	225 rows	34.9960	34.9960	< 0.0001%	Fit	3.48 ms	8.67 ms
First 225 days, minmax scaling	225 rows	34.9891	34.9891	< 0.0001%	Fit	3.17 ms	8.79 ms
First 232 days, absmax scaling	232 rows	34.9930	34.9930	< 0.0001%	Fit	2.98 ms	8.86 ms
First 232 days, minmax scaling	232 rows	34.9807	34.9807	< 0.0001%	Fit	3.20 ms	8.89 ms

Logistic growth

Each supported effect on its own, then all of them together: a weekly pattern, a switchable weekly pattern, an event with a window, and additive and multiplicative regressors. All logistic growth benchmarks →

Case	Input	Effect output	Python output	Largest difference	Timed step	Effect time	Python time
Weekly pattern	96 rows	105.2764	105.2764	< 0.0001%	Fit	15.6 ms	27.5 ms
Switchable weekly pattern	96 rows	105.2763	105.2763	< 0.0001%	Fit	16.2 ms	28.3 ms
Event with a window	96 rows	105.2751	105.2751	< 0.0001%	Fit	9.09 ms	17.9 ms
Additive regressor	96 rows	105.2753	105.2753	< 0.0001%	Fit	7.94 ms	15.8 ms
Multiplicative regressor	96 rows	105.2751	105.2751	< 0.0001%	Fit	11.3 ms	18.6 ms
Everything at once, absmax scaling	96 rows	105.8453	105.8453	< 0.0001%	Fit	29.0 ms	46.6 ms
Everything at once, minmax scaling	96 rows	105.8453	105.8453	< 0.0001%	Fit	25.3 ms	44.0 ms