Feature: Add support for logistic growth to Prophet

darts/models/forecasting/prophet_model.py

@@ -5,7 +5,7 @@
 
 import logging
 import re
-from typing import List, Optional, Union
+from typing import Callable, List, Optional, Sequence, Union
 
 import numpy as np
 import pandas as pd
@@ -28,6 +28,12 @@ def __init__(
         country_holidays: Optional[str] = None,
         suppress_stdout_stderror: bool = True,
         add_encoders: Optional[dict] = None,
+        cap: Union[
+            float, Callable[[Union[pd.DatetimeIndex, pd.RangeIndex]], Sequence[float]]
+        ] = None,
+        floor: Union[
+            float, Callable[[Union[pd.DatetimeIndex, pd.RangeIndex]], Sequence[float]]
+        ] = None,
         **prophet_kwargs,
     ):
         """Facebook Prophet
@@ -92,6 +98,26 @@ def __init__(
                     'transformer': Scaler()
                 }
             ..
+        cap
+            Parameter specifiying the maximum carrying capacity when predicting with logistic growth.
+            Mandatory when `growth = 'logistic'`, otherwise ignored.
+            See <https://facebook.github.io/prophet/docs/saturating_forecasts.html> for more information
+            on logistic forecasts.
+            Can be either
+
+            - a number, for constant carrying capacities
+            - a function taking a DatetimeIndex or RangeIndex and returning a corresponding a Sequence of numbers,
+            where each number indicates the carrying capacity at this index.
+        floor
+            Parameter specifiying the minimum carrying capacity when predicting logistic growth.
+            Optional when `growth = 'logistic'` (defaults to 0), otherwise ignored.
+            See <https://facebook.github.io/prophet/docs/saturating_forecasts.html> for more information
+            on logistic forecasts.
+            Can be either
+
+            - a number, for constant carrying capacities
+            - a function taking a DatetimeIndex or RangeIndex and returning a corresponding a Sequence of numbers,
+            where each number indicates the carrying capacity at this index.
         prophet_kwargs
             Some optional keyword arguments for Prophet.
             For information about the parameters see:
@@ -119,6 +145,26 @@ def __init__(
         self._execute_and_suppress_output = execute_and_suppress_output
         self._model_builder = prophet.Prophet
 
+        self._cap = cap
+        self._floor = floor
+        self.is_logistic = (
+            "growth" in prophet_kwargs and prophet_kwargs["growth"] == "logistic"
+        )
+        if not self.is_logistic and (cap is not None or floor is not None):
+            logger.warning(
+                "Parameters `cap` and/or `floor` were set although `growth` is not "
+                "logistic. The set capacities will be ignored."
+            )
+        if self.is_logistic:
+            raise_if(
+                cap is None,
+                "Parameter `cap` has to be set when `growth` is logistic",
+                logger,
+            )
+            if floor is None:
+                # Use 0 as default value
+                self._floor = 0
+
     def __str__(self):
         return "Prophet"
 
@@ -131,6 +177,8 @@ def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = Non
         fit_df = pd.DataFrame(
             data={"ds": series.time_index, "y": series.univariate_values()}
         )
+        if self.is_logistic:
+            fit_df = self._add_capacities_to_df(fit_df)
 
         self.model = self._model_builder(**self.prophet_kwargs)
 
@@ -188,13 +236,31 @@ def _predict(
 
         return self._build_forecast_series(forecast)
 
+    def _add_capacities_to_df(self, df: pd.DataFrame) -> pd.DataFrame:
+        dates = df["ds"]
+        try:
+            df["cap"] = self._cap(dates) if callable(self._cap) else self._cap
+            df["floor"] = self._floor(dates) if callable(self._floor) else self._floor
+        except ValueError as e:
+            raise_if(
+                "does not match length of index" in str(e),
+                "Callables supplied to `Prophet.set_capacity` as `cap` or `floor` "
+                "arguments have to return Sequences of identical length as their "
+                " input argument Sequence!",
+                logger,
+            )
+            raise
+        return df
+
     def _generate_predict_df(
         self, n: int, future_covariates: Optional[TimeSeries] = None
     ) -> pd.DataFrame:
         """Returns a pandas DataFrame in the format required for Prophet.predict() with `n` dates after the end of
         the fitted TimeSeries"""
 
         predict_df = pd.DataFrame(data={"ds": self._generate_new_dates(n)})
+        if self.is_logistic:
+            predict_df = self._add_capacities_to_df(predict_df)
         if future_covariates is not None:
             predict_df = predict_df.merge(
                 future_covariates.pd_dataframe(),

darts/tests/models/forecasting/test_prophet.py

@@ -133,6 +133,27 @@ def test_prophet_model_default_with_prophet_constructor(self):
         model = Prophet()
         assert model._model_builder == FBProphet, "model should use Facebook Prophet"
 
+    def test_prophet_model_with_logistic_growth(self):
+        model = Prophet(growth="logistic", cap=1)
+
+        # Create timeseries with logistic function
+        times = tg.generate_index(
+            pd.Timestamp("20200101"), pd.Timestamp("20210101"), freq="D"
+        )
+        values = np.linspace(-10, 10, len(times))
+        f = np.vectorize(lambda x: 1 / (1 + np.exp(-x)))
+        values = f(values)
+        ts = TimeSeries.from_times_and_values(times, values, freq="D")
+        # split in the middle, so the only way of predicting the plateau correctly
+        # is using the capacity
+        train, val = ts.split_after(0.5)
+
+        model.fit(train)
+        pred = model.predict(len(val))
+
+        for val_i, pred_i in zip(val.univariate_values(), pred.univariate_values()):
+            self.assertAlmostEqual(val_i, pred_i, delta=0.1)
+
     def helper_test_freq_coversion(self, test_cases):
         for freq, period in test_cases.items():
             ts_sine = tg.sine_timeseries(