A regressor forecast by its own model
match ← All flat growth benchmarksThe same two years, with temperature forecast by its own default model, as with regressor_predictor=True. Its forecast replaces the half-year of future temperatures.
Input
benchmark/inputs/v4/flat-correlated-regressors.json: 730 history rows and 180 forecast rows.
Output
Largest difference: 0.00026 (0.0020% of the input's range).
Every forecast row from both libraries.
| Date | Effect Prophet | Python Prophet | Difference |
|---|---|---|---|
| 2021-12-31 | 44.6640 | 44.6638 | 0.00024 |
| 2022-01-01 | 43.2809 | 43.2807 | 0.00024 |
| 2022-01-02 | 41.8068 | 41.8066 | 0.00026 |
| 2022-01-03 | 41.2981 | 41.2979 | 0.00026 |
| 2022-01-04 | 42.0470 | 42.0468 | 0.00026 |
| 2022-01-05 | 43.4620 | 43.4620 | 4.0e-5 |
| 2022-01-06 | 44.4592 | 44.4591 | 0.00011 |
| 2022-01-07 | 44.1913 | 44.1910 | 0.00023 |
| 2022-01-08 | 42.8044 | 42.8042 | 0.00024 |
| 2022-01-09 | 41.3266 | 41.3263 | 0.00025 |
| 2022-01-10 | 40.8143 | 40.8141 | 0.00026 |
| 2022-01-11 | 41.5597 | 41.5594 | 0.00025 |
| 2022-01-12 | 42.9712 | 42.9712 | 3.2e-5 |
| 2022-01-13 | 43.9649 | 43.9648 | 0.00010 |
| 2022-01-14 | 43.6937 | 43.6935 | 0.00022 |
| 2022-01-15 | 42.3035 | 42.3032 | 0.00023 |
| 2022-01-16 | 40.8224 | 40.8221 | 0.00024 |
| 2022-01-17 | 40.3068 | 40.3066 | 0.00025 |
| 2022-01-18 | 41.0489 | 41.0487 | 0.00024 |
| 2022-01-19 | 42.4571 | 42.4571 | 2.4e-5 |
| 2022-01-20 | 43.4476 | 43.4475 | 9.3e-5 |
| 2022-01-21 | 43.1730 | 43.1728 | 0.00021 |
| 2022-01-22 | 41.7794 | 41.7791 | 0.00022 |
| 2022-01-23 | 40.2948 | 40.2946 | 0.00023 |
| 2022-01-24 | 39.7757 | 39.7755 | 0.00024 |
| 2022-01-25 | 40.5142 | 40.5139 | 0.00023 |
| 2022-01-26 | 41.9187 | 41.9187 | 1.6e-5 |
| 2022-01-27 | 42.9053 | 42.9053 | 8.5e-5 |
| 2022-01-28 | 42.6268 | 42.6266 | 0.00021 |
| 2022-01-29 | 41.2293 | 41.2290 | 0.00021 |
| 2022-01-30 | 39.7406 | 39.7404 | 0.00023 |
| 2022-01-31 | 39.2174 | 39.2171 | 0.00023 |
| 2022-02-01 | 39.9516 | 39.9513 | 0.00023 |
| 2022-02-02 | 41.3518 | 41.3518 | 8.7e-6 |
| 2022-02-03 | 42.3340 | 42.3339 | 7.8e-5 |
| 2022-02-04 | 42.0511 | 42.0509 | 0.00020 |
| 2022-02-05 | 40.6490 | 40.6488 | 0.00021 |
| 2022-02-06 | 39.1558 | 39.1556 | 0.00022 |
| 2022-02-07 | 38.6280 | 38.6278 | 0.00022 |
| 2022-02-08 | 39.3576 | 39.3574 | 0.00022 |
| 2022-02-09 | 40.7533 | 40.7533 | 9.4e-7 |
| 2022-02-10 | 41.7310 | 41.7309 | 7.0e-5 |
| 2022-02-11 | 41.4436 | 41.4434 | 0.00019 |
| 2022-02-12 | 40.0370 | 40.0368 | 0.00020 |
| 2022-02-13 | 38.5395 | 38.5392 | 0.00021 |
| 2022-02-14 | 38.0073 | 38.0071 | 0.00022 |
| 2022-02-15 | 38.7327 | 38.7324 | 0.00021 |
| 2022-02-16 | 40.1241 | 40.1241 | 6.8e-6 |
| 2022-02-17 | 41.0977 | 41.0976 | 6.2e-5 |
| 2022-02-18 | 40.8062 | 40.8060 | 0.00018 |
| 2022-02-19 | 39.3956 | 39.3954 | 0.00019 |
| 2022-02-20 | 37.8941 | 37.8939 | 0.00020 |
| 2022-02-21 | 37.3581 | 37.3579 | 0.00021 |
| 2022-02-22 | 38.0797 | 38.0795 | 0.00020 |
| 2022-02-23 | 39.4674 | 39.4674 | 1.5e-5 |
| 2022-02-24 | 40.4373 | 40.4372 | 5.4e-5 |
| 2022-02-25 | 40.1421 | 40.1419 | 0.00017 |
| 2022-02-26 | 38.7279 | 38.7277 | 0.00018 |
| 2022-02-27 | 37.2228 | 37.2226 | 0.00020 |
| 2022-02-28 | 36.6832 | 36.6830 | 0.00020 |
| 2022-03-01 | 37.4012 | 37.4010 | 0.00019 |
| 2022-03-02 | 38.7854 | 38.7854 | 2.2e-5 |
| 2022-03-03 | 39.7516 | 39.7516 | 4.6e-5 |
| 2022-03-04 | 39.4529 | 39.4527 | 0.00017 |
| 2022-03-05 | 38.0351 | 38.0350 | 0.00017 |
| 2022-03-06 | 36.5265 | 36.5263 | 0.00019 |
| 2022-03-07 | 35.9833 | 35.9831 | 0.00019 |
| 2022-03-08 | 36.6976 | 36.6974 | 0.00019 |
| 2022-03-09 | 38.0781 | 38.0781 | 3.0e-5 |
| 2022-03-10 | 39.0407 | 39.0407 | 3.9e-5 |
| 2022-03-11 | 38.7384 | 38.7382 | 0.00016 |
| 2022-03-12 | 37.3170 | 37.3168 | 0.00017 |
| 2022-03-13 | 35.8046 | 35.8045 | 0.00018 |
| 2022-03-14 | 35.2578 | 35.2576 | 0.00019 |
| 2022-03-15 | 35.9686 | 35.9684 | 0.00018 |
| 2022-03-16 | 37.3455 | 37.3456 | 3.8e-5 |
| 2022-03-17 | 38.3047 | 38.3046 | 3.1e-5 |
| 2022-03-18 | 37.9988 | 37.9987 | 0.00015 |
| 2022-03-19 | 36.5740 | 36.5739 | 0.00016 |
| 2022-03-20 | 35.0584 | 35.0582 | 0.00017 |
| 2022-03-21 | 34.5083 | 34.5081 | 0.00018 |
| 2022-03-22 | 35.2159 | 35.2157 | 0.00017 |
| 2022-03-23 | 36.5898 | 36.5898 | 4.6e-5 |
| 2022-03-24 | 37.5459 | 37.5459 | 2.3e-5 |
| 2022-03-25 | 37.2371 | 37.2370 | 0.00014 |
| 2022-03-26 | 35.8095 | 35.8093 | 0.00015 |
| 2022-03-27 | 34.2911 | 34.2909 | 0.00016 |
| 2022-03-28 | 33.7384 | 33.7382 | 0.00017 |
| 2022-03-29 | 34.4434 | 34.4432 | 0.00016 |
| 2022-03-30 | 35.8147 | 35.8148 | 5.3e-5 |
| 2022-03-31 | 36.7685 | 36.7684 | 1.5e-5 |
| 2022-04-01 | 36.4574 | 36.4572 | 0.00014 |
| 2022-04-02 | 35.0275 | 35.0273 | 0.00014 |
| 2022-04-03 | 33.5069 | 33.5067 | 0.00016 |
| 2022-04-04 | 32.9520 | 32.9518 | 0.00016 |
| 2022-04-05 | 33.6549 | 33.6547 | 0.00016 |
| 2022-04-06 | 35.0242 | 35.0243 | 6.1e-5 |
| 2022-04-07 | 35.9759 | 35.9759 | 7.7e-6 |
| 2022-04-08 | 35.6628 | 35.6627 | 0.00013 |
| 2022-04-09 | 34.2310 | 34.2309 | 0.00014 |
| 2022-04-10 | 32.7085 | 32.7083 | 0.00015 |
| 2022-04-11 | 32.1517 | 32.1516 | 0.00016 |
| 2022-04-12 | 32.8528 | 32.8527 | 0.00015 |
| 2022-04-13 | 34.2203 | 34.2204 | 6.9e-5 |
| 2022-04-14 | 35.1702 | 35.1702 | 7.7e-8 |
| 2022-04-15 | 34.8554 | 34.8553 | 0.00012 |
| 2022-04-16 | 33.4219 | 33.4218 | 0.00013 |
| 2022-04-17 | 31.8977 | 31.8976 | 0.00014 |
| 2022-04-18 | 31.3394 | 31.3392 | 0.00015 |
| 2022-04-19 | 32.0389 | 32.0387 | 0.00014 |
| 2022-04-20 | 33.4049 | 33.4050 | 7.7e-5 |
| 2022-04-21 | 34.3534 | 34.3534 | 7.8e-6 |
| 2022-04-22 | 34.0372 | 34.0371 | 0.00011 |
| 2022-04-23 | 32.6024 | 32.6023 | 0.00012 |
| 2022-04-24 | 31.0770 | 31.0769 | 0.00013 |
| 2022-04-25 | 30.5175 | 30.5174 | 0.00014 |
| 2022-04-26 | 31.2160 | 31.2159 | 0.00013 |
| 2022-04-27 | 32.5811 | 32.5812 | 8.4e-5 |
| 2022-04-28 | 33.5287 | 33.5288 | 1.6e-5 |
| 2022-04-29 | 33.2118 | 33.2117 | 0.00010 |
| 2022-04-30 | 31.7764 | 31.7763 | 0.00011 |
| 2022-05-01 | 30.2505 | 30.2504 | 0.00013 |
| 2022-05-02 | 29.6906 | 29.6904 | 0.00013 |
| 2022-05-03 | 30.3887 | 30.3886 | 0.00012 |
| 2022-05-04 | 31.7535 | 31.7536 | 9.2e-5 |
| 2022-05-05 | 32.7011 | 32.7011 | 2.3e-5 |
| 2022-05-06 | 32.3841 | 32.3840 | 9.7e-5 |
| 2022-05-07 | 30.9486 | 30.9485 | 0.00010 |
| 2022-05-08 | 29.4228 | 29.4227 | 0.00012 |
| 2022-05-09 | 28.8631 | 28.8629 | 0.00012 |
| 2022-05-10 | 29.5614 | 29.5613 | 0.00012 |
| 2022-05-11 | 30.9266 | 30.9267 | 1.0e-4 |
| 2022-05-12 | 31.8744 | 31.8744 | 3.1e-5 |
| 2022-05-13 | 31.5578 | 31.5577 | 8.9e-5 |
| 2022-05-14 | 30.1228 | 30.1227 | 9.7e-5 |
| 2022-05-15 | 28.5975 | 28.5974 | 0.00011 |
| 2022-05-16 | 28.0383 | 28.0382 | 0.00012 |
| 2022-05-17 | 28.7373 | 28.7372 | 0.00011 |
| 2022-05-18 | 30.1030 | 30.1031 | 0.00011 |
| 2022-05-19 | 31.0515 | 31.0516 | 3.9e-5 |
| 2022-05-20 | 30.7357 | 30.7356 | 8.2e-5 |
| 2022-05-21 | 29.3014 | 29.3014 | 8.9e-5 |
| 2022-05-22 | 27.7769 | 27.7768 | 0.00010 |
| 2022-05-23 | 27.2186 | 27.2185 | 0.00011 |
| 2022-05-24 | 27.9185 | 27.9184 | 0.00010 |
| 2022-05-25 | 29.2852 | 29.2853 | 0.00012 |
| 2022-05-26 | 30.2348 | 30.2348 | 4.7e-5 |
| 2022-05-27 | 29.9201 | 29.9200 | 7.4e-5 |
| 2022-05-28 | 28.4870 | 28.4869 | 8.1e-5 |
| 2022-05-29 | 26.9638 | 26.9637 | 9.5e-5 |
| 2022-05-30 | 26.4068 | 26.4067 | 0.00010 |
| 2022-05-31 | 27.1081 | 27.1080 | 9.4e-5 |
| 2022-06-01 | 28.4764 | 28.4765 | 0.00012 |
| 2022-06-02 | 29.4276 | 29.4276 | 5.4e-5 |
| 2022-06-03 | 29.1145 | 29.1145 | 6.6e-5 |
| 2022-06-04 | 27.6833 | 27.6832 | 7.4e-5 |
| 2022-06-05 | 26.1619 | 26.1619 | 8.7e-5 |
| 2022-06-06 | 25.6069 | 25.6068 | 9.3e-5 |
| 2022-06-07 | 26.3103 | 26.3102 | 8.6e-5 |
| 2022-06-08 | 27.6807 | 27.6808 | 0.00013 |
| 2022-06-09 | 28.6341 | 28.6342 | 6.2e-5 |
| 2022-06-10 | 28.3234 | 28.3233 | 5.8e-5 |
| 2022-06-11 | 26.8945 | 26.8944 | 6.6e-5 |
| 2022-06-12 | 25.3756 | 25.3755 | 7.9e-5 |
| 2022-06-13 | 24.8231 | 24.8230 | 8.5e-5 |
| 2022-06-14 | 25.5290 | 25.5289 | 7.8e-5 |
| 2022-06-15 | 26.9020 | 26.9021 | 0.00014 |
| 2022-06-16 | 27.8580 | 27.8581 | 7.0e-5 |
| 2022-06-17 | 27.5500 | 27.5499 | 5.0e-5 |
| 2022-06-18 | 26.1238 | 26.1238 | 5.8e-5 |
| 2022-06-19 | 24.6077 | 24.6076 | 7.2e-5 |
| 2022-06-20 | 24.0579 | 24.0578 | 7.8e-5 |
| 2022-06-21 | 24.7667 | 24.7666 | 7.0e-5 |
| 2022-06-22 | 26.1425 | 26.1426 | 0.00015 |
| 2022-06-23 | 27.1014 | 27.1015 | 7.8e-5 |
| 2022-06-24 | 26.7963 | 26.7962 | 4.3e-5 |
| 2022-06-25 | 25.3730 | 25.3730 | 5.0e-5 |
| 2022-06-26 | 23.8598 | 23.8597 | 6.4e-5 |
| 2022-06-27 | 23.3130 | 23.3130 | 7.0e-5 |
| 2022-06-28 | 24.0248 | 24.0247 | 6.3e-5 |
Timing
Median of all measured repetitions.
| Step | Effect Prophet | Python Prophet | Effect Prophet is |
|---|---|---|---|
| Convert input rows | 222 µs | 936 µs | 4.2× faster |
| Fit | 24.2 ms | 100 ms | 4.1× faster |
| Predict | 1.08 ms | 27.2 ms | 25× faster |
| Fit and predict | 24.5 ms | 127 ms | 5.2× faster |
| Convert, fit and predict | 23.7 ms | 129 ms | 5.4× faster |
| First forecast in a new process | 209 ms | 1.56 s | 7.5× faster |
Settings
The case declaration both libraries ran.
json
{
"kind": "map",
"comparison": {
"kind": "equivalent-objective",
"evidenceId": "flat-map-additive-v1"
},
"configuration": {
"growth": "flat",
"scaling": "absmax",
"seasonalityMode": "additive",
"holidaysMode": "additive",
"changepoints": {
"mode": "auto",
"count": 25,
"range": 0.8
},
"changepointPriorScale": 0.05,
"seasonalities": [
{
"name": "yearly-custom",
"periodDays": 365.25,
"fourierOrder": 10,
"priorScale": 10
},
{
"name": "weekly-custom",
"periodDays": 7,
"fourierOrder": 2,
"priorScale": 10
}
],
"events": [],
"regressors": [
{
"name": "temperature",
"priorScale": 10,
"standardization": "auto",
"predictor": true
},
{
"name": "baseline",
"priorScale": 10,
"standardization": "never"
}
]
},
"pythonOptimizer": {
"algorithm": "Auto",
"maxIterations": 10000,
"newtonFallback": true,
"sigFigs": 12
}
}