Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
This report presents the forensic synthetic code analysis of facebook/prophet, a Python project with 20,333 GitHub stars. SynthScan v2.0 examined 30,544 lines of code across 160 source files, recording 160 pattern matches distributed across 10 syntactic categories. The overall adjusted score of 6.0 places this repository in the Low AI signal band.
The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).
Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.
Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.
This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.
The scanner identified 160 distinct pattern matches across 10 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/setup.py | 62 | def maybe_install_cmdstan_toolchain() -> bool: | CODE |
| LOW | python/prophet/serialize.py | 139 | def _handle_simple_attributes_backwards_compat(model_dict: dict[str, Any]) -> None: | CODE |
| LOW | python/prophet/plot.py | 557 | def plot_cross_validation_metric( | CODE |
| LOW | python/prophet/plot.py | 884 | def plot_forecast_component_plotly( | CODE |
| LOW | python/prophet/plot.py | 955 | def get_forecast_component_plotly_props( | CODE |
| LOW | python/prophet/plot.py | 1070 | def get_seasonality_plotly_props( | CODE |
| LOW | python/prophet/diagnostics.py | 382 | def register_performance_metric(func: _PerformanceMetricT) -> _PerformanceMetricT: | CODE |
| LOW | python/prophet/forecaster.py | 540 | def make_seasonality_features( | CODE |
| LOW | python/prophet/forecaster.py | 568 | def construct_holiday_dataframe(self, dates: pd.Series[pd.Timestamp]) -> pd.DataFrame: | CODE |
| LOW | python/prophet/forecaster.py | 782 | def _prepare_regressors_for_predict( | CODE |
| LOW | python/prophet/forecaster.py | 960 | def make_all_seasonality_features(self, df: pd.DataFrame) -> tuple[ | CODE |
| LOW | python/prophet/forecaster.py | 1637 | def predict_seasonal_components(self, df: pd.DataFrame) -> pd.DataFrame: | CODE |
| LOW | python/prophet/forecaster.py | 1705 | def sample_posterior_predictive( | CODE |
| LOW | python/prophet/forecaster.py | 1923 | def sample_predictive_trend_vectorized( | CODE |
| LOW | python/prophet/forecaster.py | 2048 | def _make_historical_mat_time( | CODE |
| LOW | python/prophet/make_holidays.py | 14 | def get_country_holidays_class(country: str) -> type[holidays.HolidayBase]: | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 87 | def test_bad_parallel_methods(self, ts_short, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 97 | def test_check_single_cutoff_forecast_func_calls(self, ts_short, monkeypatch, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 127 | def test_check_extra_output_columns_cross_validation(self, ts_short, backend, extra_output_columns): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 140 | def test_cross_validation_logistic_or_flat_growth(self, growth, ts_short, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 153 | def test_cross_validation_extra_regressors(self, ts_short, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 180 | def test_cross_validation_metrics_with_regressor_predictor(self, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 208 | def test_cross_validation_with_regressor_predictor(self, backend, monkeypatch): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 239 | def test_cross_validation_default_value_check(self, ts_short, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 250 | def test_cross_validation_custom_cutoffs(self, ts_short, backend): | CODE |
| LOW | python/prophet/tests/test_diagnostics.py | 263 | def test_cross_validation_uncertainty_disabled(self, ts_short, backend): | CODE |
| LOW | python/prophet/tests/conftest.py | 39 | def pytest_collection_modifyitems(config, items): | CODE |
| LOW | python/prophet/tests/test_utilities.py | 13 | def test_regressor_coefficients(self, daily_univariate_ts, backend): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 109 | def test_fit_predict_no_changepoints(self, daily_univariate_ts, backend): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 119 | def test_fit_predict_no_changepoints_mcmc(self, daily_univariate_ts, backend): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 128 | def test_fit_changepoint_not_in_history(self, daily_univariate_ts, backend): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 448 | def test_fourier_series_weekly(self, daily_univariate_ts): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 454 | def test_fourier_series_yearly(self, daily_univariate_ts): | CODE |
| LOW⚡ | python/prophet/tests/test_prophet.py | 462 | def test_auto_weekly_seasonality(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 73 | def test_fit_predict_large_numbers(self, large_numbers_ts, backend, scaling, expected): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 85 | def test_fit_predict_sampling(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 98 | def test_fit_predict_no_seasons(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 140 | def test_fit_predict_duplicates(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 154 | def test_fit_predict_constant_history(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 166 | def test_fit_predict_uncertainty_disabled(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 198 | def test_setup_dataframe_ds_column(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 223 | def test_logistic_floor_minmax(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 243 | def test_make_future_dataframe(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 257 | def test_make_future_dataframe_include_history(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 269 | def test_invalid_growth_input(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 410 | def test_set_changepoint_range(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 426 | def test_get_zero_changepoints(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 436 | def test_override_n_changepoints(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 499 | def test_auto_yearly_seasonality(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 530 | def test_yearly_seasonality_warns_on_short_history(self, caplog, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 544 | def test_yearly_seasonality_no_warning_on_auto(self, caplog, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 553 | def test_auto_daily_seasonality(self, daily_univariate_ts, subdaily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 587 | def test_set_seasonality_mode(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 649 | def test_custom_monthly_seasonality(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 660 | def test_duplicate_component_names(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 676 | def test_custom_fourier_order(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 723 | def test_conditional_custom_seasonality(self, daily_univariate_ts, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 765 | def test_holidays_lower_window(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 782 | def test_holidays_upper_window(self, backend): | CODE |
| LOW | python/prophet/tests/test_prophet.py | 873 | def test_fit_predict_with_country_holidays(self, daily_univariate_ts, backend): | CODE |
| 5 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | R/R/plot.R | 1 | # Copyright (c) Facebook, Inc. and its affiliates. | COMMENT |
| LOW | R/R/plot.R | 21 | } | COMMENT |
| LOW | R/R/plot.R | 81 | #' | COMMENT |
| LOW | R/R/plot.R | 161 | 1))) | COMMENT |
| LOW | R/R/plot.R | 241 | } | COMMENT |
| LOW | R/R/plot.R | 281 | } | COMMENT |
| LOW | R/R/plot.R | 401 | #' significant. (Default 0.01) | COMMENT |
| LOW | R/R/plot.R | 441 | #' | COMMENT |
| LOW | R/R/plot.R | 501 | attachAtBottom = TRUE) | COMMENT |
| LOW | R/R/plot.R | 521 | #' As a concrete example, if metric='mse', then this plot will show the | COMMENT |
| LOW | R/R/prophet.R | 21 | #' provided, then the model object will be instantiated but not fit; use | COMMENT |
| LOW | R/R/prophet.R | 41 | #' @param holidays data frame with columns holiday (character) and ds (date | COMMENT |
| LOW | R/R/prophet.R | 61 | #' the trend using the MAP estimate of the extrapolated generative model. If | COMMENT |
| LOW | R/R/prophet.R | 281 | COMMENT | |
| LOW | R/R/prophet.R | 301 | #' Prepare dataframe for fitting or predicting. | COMMENT |
| LOW | R/R/prophet.R | 381 | df[[name]] <- (df[[name]] - props$mu) / props$std | COMMENT |
| LOW | R/R/prophet.R | 561 | #' @param holidays Dataframe containing holidays, as returned by | COMMENT |
| LOW | R/R/prophet.R | 641 | #' Decreasing the prior scale will add additional regularization. If no | COMMENT |
| LOW | R/R/prophet.R | 701 | #' | COMMENT |
| LOW | R/R/prophet.R | 881 | COMMENT | |
| LOW | R/R/prophet.R | 981 | COMMENT | |
| LOW | R/R/prophet.R | 1081 | #' | COMMENT |
| LOW | R/R/prophet.R | 1101 | #' last points in the time series. | COMMENT |
| LOW | R/R/prophet.R | 1121 | #' Initialize logistic growth. | COMMENT |
| LOW | R/R/prophet.R | 1161 | #' | COMMENT |
| LOW | R/R/prophet.R | 1281 | return(m) | COMMENT |
| LOW | R/R/prophet.R | 1341 | #' Evaluate the flat trend function. | COMMENT |
| LOW | R/R/prophet.R | 1381 | #' | COMMENT |
| LOW | R/R/prophet.R | 1521 | #' @param df Dataframe with dates for predictions (column ds), and capacity | COMMENT |
| LOW | R/R/prophet.R | 1561 | #' Simulate observations from the extrapolated generative model. | COMMENT |
| LOW | R/R/prophet.R | 1641 | #' @param m Prophet model object. | COMMENT |
| LOW | R/R/utilities.R | 1 | # Copyright (c) Facebook, Inc. and its affiliates. | COMMENT |
| LOW | R/R/utilities.R | 21 | #' \item{center: The mean of the regressor if it was standardized. Otherwise 0.} | COMMENT |
| LOW | R/R/data.R | 1 | # Copyright (c) Facebook, Inc. and its affiliates. | COMMENT |
| LOW | R/R/stan_backends.R | 81 | #' @param model Model object. | COMMENT |
| LOW | R/R/stan_backends.R | 161 | #' @return A named list containing "stan_fit" (the fitted stan object), | COMMENT |
| LOW | R/R/diagnostics.R | 1 | # Copyright (c) Facebook, Inc. and its affiliates. | COMMENT |
| LOW | R/R/diagnostics.R | 61 | #' technique described in https://robjhyndman.com/hyndsight/tscv/ . | COMMENT |
| LOW | R/R/diagnostics.R | 141 | COMMENT | |
| LOW | R/R/diagnostics.R | 181 | # Merge yhat, y, and cutoff. | COMMENT |
| LOW | R/R/diagnostics.R | 241 | #' | COMMENT |
| LOW | R/R/diagnostics.R | 261 | #' will compute it separately for each horizon. The default of | COMMENT |
| LOW | R/R/diagnostics.R | 501 | #' Mean absolute percent error | COMMENT |
| LOW | R/R/diagnostics.R | 521 | #' @param w Aggregation window size. | COMMENT |
| LOW | R/R/diagnostics.R | 541 | #' @return Array of symmetric mean absolute percent errors. | COMMENT |
| LOW | docs/_config.yml | 21 | # https://github.com/blog/2277-what-s-new-in-github-pages-with-jekyll-3-3 | COMMENT |
| LOW | docs/_config.yml | 41 | secondary: "#f9f9f9" | COMMENT |
| LOW | docs/_data/nav_docs.yml | 21 | # - id: C | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | python/prophet/forecaster.py | 1098 | if not component_cols.equals(self.train_component_cols): | CODE |
| HIGH | python/prophet/tests/test_diagnostics.py | 430 | assert m1.changepoints.equals(m2.changepoints) | CODE |
| HIGH | python/prophet/tests/test_serialize.py | 41 | assert v.equals(m2.__dict__[k]) | CODE |
| HIGH | python/prophet/tests/test_serialize.py | 115 | assert v.equals(m2.__dict__[k]) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/prophet/serialize.py | 6 | CODE | |
| LOW | python/prophet/plot.py | 6 | CODE | |
| LOW | python/prophet/models.py | 6 | CODE | |
| LOW | python/prophet/diagnostics.py | 6 | CODE | |
| LOW | python/prophet/diagnostics.py | 30 | CODE | |
| LOW | python/prophet/__init__.py | 6 | CODE | |
| LOW | python/prophet/__init__.py | 7 | CODE | |
| LOW | python/prophet/forecaster.py | 6 | CODE | |
| LOW | python/prophet/utilities.py | 6 | CODE | |
| LOW | python/prophet/make_holidays.py | 6 | CODE | |
| LOW | python_shim/fbprophet/serialize.py | 6 | CODE | |
| LOW | python_shim/fbprophet/plot.py | 6 | CODE | |
| LOW | python_shim/fbprophet/models.py | 6 | CODE | |
| LOW | python_shim/fbprophet/diagnostics.py | 6 | CODE | |
| LOW | python_shim/fbprophet/__init__.py | 8 | CODE | |
| LOW | python_shim/fbprophet/forecaster.py | 6 | CODE | |
| LOW | python_shim/fbprophet/make_holidays.py | 6 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | python/prophet/diagnostics.py | 0 | mean absolute scale error parameters ---------- df: cross-validation results dataframe. w: aggregation window size. retu | STRING |
| HIGH | python/prophet/tests/test_diagnostics.py | 0 | mean absolute scale error parameters ---------- df: cross-validation results dataframe. w: aggregation window size. retu | STRING |
| HIGH | docs/_docs/diagnostics.md | 0 | mean absolute scale error parameters ---------- df: cross-validation results dataframe. w: aggregation window size. retu | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/prophet/serialize.py | 42 | CODE | |
| LOW | python/prophet/serialize.py | 149 | CODE | |
| LOW | python/prophet/plot.py | 127 | CODE | |
| LOW | python/prophet/plot.py | 431 | CODE | |
| LOW | python/prophet/diagnostics.py | 93 | CODE | |
| LOW | python/prophet/forecaster.py | 1705 | CODE | |
| LOW | python/prophet/forecaster.py | 1968 | CODE | |
| LOW | python/prophet/tests/test_serialize.py | 18 | CODE | |
| LOW | python/prophet/tests/test_serialize.py | 60 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/prophet/diagnostics.py | 204 | # Check if the initial window | COMMENT |
| LOW | python/scripts/generate_holidays_file.py | 24 | # Check if anything converted | COMMENT |
| LOW | R/R/diagnostics.R | 124 | # Check if the initial window (that is, the amount of time between the | COMMENT |
| LOW | docs/api/prophet/diagnostics.html | 308 | </span><span id="L-204"><a href="#L-204"><span class="linenos">204</span></a> <span class="c1"># Check if the initial | CODE |
| LOW | docs/api/prophet/diagnostics.html | 1081 | </span><span id="cross_validation-205"><a href="#cross_validation-205"><span class="linenos">205</span></a> <span cla | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/prophet/models.py | 74 | def set_options(self, **kwargs: Unpack[_IStanBackendOptions]) -> None: | CODE |
| LOW | python/prophet/__init__.py | 9 | __all__ = ["Prophet", "__version__"] | CODE |
| LOW | python/prophet/forecaster.py | 459 | def set_changepoints(self) -> None: | CODE |
| LOW | python/prophet/forecaster.py | 1171 | def set_auto_seasonalities(self) -> None: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/setup.py | 69 | except Exception: | CODE |
| LOW | python/prophet/models.py | 244 | except Exception: | CODE |
| LOW | python/prophet/forecaster.py | 210 | except Exception as e: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | python/prophet/plot.py | 647 | CODE | |
| LOW | python/prophet/forecaster.py | 130 | CODE |