We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[BUG] forecasting API docs page is broken
**Describe the bug**
<!--
No ARIMA, BATS and Prophet documentation in the API References
-->


Hello, 
In your API documentation (https://www.sktime.org/en/v0.10.0/api_reference/forecasting.html) there is no references for prophet, ARIMA and BATS/TBATS


![unknown](https://user-images.githubusercontent.com/89520785/152760511-00c6d7db-9114-4efb-9225-921ad5d45e89.png)


</details>

<!-- Thanks for contributing! -->


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `sktime/forecasting/fbprophet.py`
Content:
```
1 #!/usr/bin/env python3 -u
2 # -*- coding: utf-8 -*-
3 # copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
4 """Implements Prophet forecaster by wrapping fbprophet."""
5 
6 __author__ = ["aiwalter"]
7 __all__ = ["Prophet"]
8 
9 
10 from sktime.forecasting.base._base import DEFAULT_ALPHA
11 from sktime.forecasting.base.adapters import _ProphetAdapter
12 from sktime.utils.validation._dependencies import _check_soft_dependencies
13 
14 _check_soft_dependencies("prophet", severity="warning")
15 
16 
17 class Prophet(_ProphetAdapter):
18     """Prophet forecaster by wrapping Facebook's prophet algorithm [1]_.
19 
20     Parameters
21     ----------
22     freq: String of DatetimeIndex frequency. Refer [2]_ for possible values:
23     add_seasonality: dict or None, default=None
24         Dict with args for Prophet.add_seasonality().
25         Dict can have the following keys/values:
26             * name: string name of the seasonality component.
27             * period: float number of days in one period.
28             * fourier_order: int number of Fourier components to use.
29             * prior_scale: optional float prior scale for this component.
30             * mode: optional 'additive' or 'multiplicative'
31             * condition_name: string name of the seasonality condition.
32     add_country_holidays: dict or None, default=None
33         Dict with args for Prophet.add_country_holidays().
34         Dict can have the following keys/values:
35             country_name: Name of the country, like 'UnitedStates' or 'US'
36     growth: str, default="linear"
37         String 'linear' or 'logistic' to specify a linear or logistic
38         trend.
39     changepoints: list or None, default=None
40         List of dates at which to include potential changepoints. If
41         not specified, potential changepoints are selected automatically.
42     n_changepoints: int, default=25
43         Number of potential changepoints to include. Not used
44         if input `changepoints` is supplied. If `changepoints` is not supplied,
45         then n_changepoints potential changepoints are selected uniformly from
46         the first `changepoint_range` proportion of the history.
47     changepoint_range: float, default=0.8
48         Proportion of history in which trend changepoints will
49         be estimated. Defaults to 0.8 for the first 80%. Not used if
50         `changepoints` is specified.
51     yearly_seasonality: str or bool or int, default="auto"
52         Fit yearly seasonality.
53         Can be 'auto', True, False, or a number of Fourier terms to generate.
54     weekly_seasonality: str or bool or int, default="auto"
55         Fit weekly seasonality.
56         Can be 'auto', True, False, or a number of Fourier terms to generate.
57     daily_seasonality: str or bool or int, default="auto"
58         Fit daily seasonality.
59         Can be 'auto', True, False, or a number of Fourier terms to generate.
60     holidays: pd.DataFrame or None, default=None
61         pd.DataFrame with columns holiday (string) and ds (date type)
62         and optionally columns lower_window and upper_window which specify a
63         range of days around the date to be included as holidays.
64         lower_window=-2 will include 2 days prior to the date as holidays. Also
65         optionally can have a column prior_scale specifying the prior scale for
66         that holiday.
67     seasonality_mode: str, default='additive'
68         Take one of 'additive' or 'multiplicative'.
69     seasonality_prior_scale: float, default=10.0
70         Parameter modulating the strength of the seasonality model.
71         Larger values allow the model to fit larger seasonal
72         fluctuations, smaller values dampen the seasonality. Can be specified
73         for individual seasonalities using add_seasonality.
74     holidays_prior_scale: float, default=10.0
75         Parameter modulating the strength of the holiday
76         components model, unless overridden in the holidays input.
77     changepoint_prior_scale: float, default=0.05
78         Parameter modulating the flexibility of the
79         automatic changepoint selection. Large values will allow many
80         changepoints, small values will allow few changepoints.
81     mcmc_samples: int, default=0
82         If greater than 0, will do full Bayesian inference
83         with the specified number of MCMC samples. If 0, will do MAP
84         estimation.
85     alpha: float, default=0.05
86         Width of the uncertainty intervals provided
87         for the forecast. If mcmc_samples=0, this will be only the uncertainty
88         in the trend using the MAP estimate of the extrapolated generative
89         model. If mcmc.samples>0, this will be integrated over all model
90         parameters, which will include uncertainty in seasonality.
91     uncertainty_samples: int, default=1000
92         Number of simulated draws used to estimate uncertainty intervals.
93         Settings this value to 0 or False will disable
94         uncertainty estimation and speed up the calculation.
95     stan_backend: str or None, default=None
96         str as defined in StanBackendEnum. If None, will try to
97         iterate over all available backends and find the working one.
98 
99     References
100     ----------
101     .. [1] https://facebook.github.io/prophet
102     .. [2] https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
103 
104     Examples
105     --------
106     >>> from sktime.datasets import load_airline
107     >>> from sktime.forecasting.fbprophet import Prophet
108     >>> # Prophet requires to have data with a pandas.DatetimeIndex
109     >>> y = load_airline().to_timestamp(freq='M')
110     >>> forecaster = Prophet(
111     ...     seasonality_mode='multiplicative',
112     ...     n_changepoints=int(len(y) / 12),
113     ...     add_country_holidays={'country_name': 'Germany'},
114     ...     yearly_seasonality=True)
115     >>> forecaster.fit(y)
116     Prophet(...)
117     >>> y_pred = forecaster.predict(fh=[1,2,3])
118     """
119 
120     def __init__(
121         self,
122         # Args due to wrapping
123         freq=None,
124         add_seasonality=None,
125         add_country_holidays=None,
126         # Args of fbprophet
127         growth="linear",
128         changepoints=None,
129         n_changepoints=25,
130         changepoint_range=0.8,
131         yearly_seasonality="auto",
132         weekly_seasonality="auto",
133         daily_seasonality="auto",
134         holidays=None,
135         seasonality_mode="additive",
136         seasonality_prior_scale=10.0,
137         holidays_prior_scale=10.0,
138         changepoint_prior_scale=0.05,
139         mcmc_samples=0,
140         alpha=DEFAULT_ALPHA,
141         uncertainty_samples=1000,
142         stan_backend=None,
143         verbose=0,
144         interval_width=0,
145     ):
146         _check_soft_dependencies("prophet", severity="error", object=self)
147 
148         self.freq = freq
149         self.add_seasonality = add_seasonality
150         self.add_country_holidays = add_country_holidays
151 
152         self.growth = growth
153         self.changepoints = changepoints
154         self.n_changepoints = n_changepoints
155         self.changepoint_range = changepoint_range
156         self.yearly_seasonality = yearly_seasonality
157         self.weekly_seasonality = weekly_seasonality
158         self.daily_seasonality = daily_seasonality
159         self.holidays = holidays
160         self.seasonality_mode = seasonality_mode
161         self.seasonality_prior_scale = seasonality_prior_scale
162         self.changepoint_prior_scale = changepoint_prior_scale
163         self.holidays_prior_scale = holidays_prior_scale
164         self.mcmc_samples = mcmc_samples
165         self.alpha = alpha
166         self.uncertainty_samples = uncertainty_samples
167         self.stan_backend = stan_backend
168         self.verbose = verbose
169         self.interval_width = interval_width
170 
171         # import inside method to avoid hard dependency
172         from prophet.forecaster import Prophet as _Prophet
173 
174         self._ModelClass = _Prophet
175 
176         super(Prophet, self).__init__()
177 
178     def _instantiate_model(self):
179         self._forecaster = self._ModelClass(
180             growth=self.growth,
181             changepoints=self.changepoints,
182             n_changepoints=self.n_changepoints,
183             changepoint_range=self.changepoint_range,
184             yearly_seasonality=self.yearly_seasonality,
185             weekly_seasonality=self.weekly_seasonality,
186             daily_seasonality=self.daily_seasonality,
187             holidays=self.holidays,
188             seasonality_mode=self.seasonality_mode,
189             seasonality_prior_scale=float(self.seasonality_prior_scale),
190             holidays_prior_scale=float(self.holidays_prior_scale),
191             changepoint_prior_scale=float(self.changepoint_prior_scale),
192             mcmc_samples=self.mcmc_samples,
193             interval_width=1 - self.alpha,
194             uncertainty_samples=self.uncertainty_samples,
195             stan_backend=self.stan_backend,
196         )
197         return self
198 
199     @classmethod
200     def get_test_params(cls):
201         """Return testing parameter settings for the estimator.
202 
203         Returns
204         -------
205         params : dict or list of dict
206         """
207         params = {
208             "n_changepoints": 0,
209             "yearly_seasonality": False,
210             "weekly_seasonality": False,
211             "daily_seasonality": False,
212             "uncertainty_samples": 1000,
213             "verbose": False,
214         }
215         return params
216
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/sktime/forecasting/fbprophet.py b/sktime/forecasting/fbprophet.py
--- a/sktime/forecasting/fbprophet.py
+++ b/sktime/forecasting/fbprophet.py
@@ -19,7 +19,9 @@
 
     Parameters
     ----------
-    freq: String of DatetimeIndex frequency. Refer [2]_ for possible values:
+    freq: str, default=None
+        A DatetimeIndex frequency. For possible values see
+        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
     add_seasonality: dict or None, default=None
         Dict with args for Prophet.add_seasonality().
         Dict can have the following keys/values:
@@ -99,7 +101,6 @@
     References
     ----------
     .. [1] https://facebook.github.io/prophet
-    .. [2] https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html
 
     Examples
     --------

{"golden_diff": "diff --git a/sktime/forecasting/fbprophet.py b/sktime/forecasting/fbprophet.py\n--- a/sktime/forecasting/fbprophet.py\n+++ b/sktime/forecasting/fbprophet.py\n@@ -19,7 +19,9 @@\n \n     Parameters\n     ----------\n-    freq: String of DatetimeIndex frequency. Refer [2]_ for possible values:\n+    freq: str, default=None\n+        A DatetimeIndex frequency. For possible values see\n+        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n     add_seasonality: dict or None, default=None\n         Dict with args for Prophet.add_seasonality().\n         Dict can have the following keys/values:\n@@ -99,7 +101,6 @@\n     References\n     ----------\n     .. [1] https://facebook.github.io/prophet\n-    .. [2] https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n \n     Examples\n     --------\n", "issue": "[BUG] forecasting API docs page is broken\n**Describe the bug**\r\n<!--\r\nNo ARIMA, BATS and Prophet documentation in the API References\r\n-->\r\n\r\n\r\nHello, \r\nIn your API documentation (https://www.sktime.org/en/v0.10.0/api_reference/forecasting.html) there is no references for prophet, ARIMA and BATS/TBATS\r\n\r\n\r\n![unknown](https://user-images.githubusercontent.com/89520785/152760511-00c6d7db-9114-4efb-9225-921ad5d45e89.png)\r\n\r\n\r\n</details>\r\n\r\n<!-- Thanks for contributing! -->\r\n\n", "before_files": [{"content": "#!/usr/bin/env python3 -u\n# -*- coding: utf-8 -*-\n# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)\n\"\"\"Implements Prophet forecaster by wrapping fbprophet.\"\"\"\n\n__author__ = [\"aiwalter\"]\n__all__ = [\"Prophet\"]\n\n\nfrom sktime.forecasting.base._base import DEFAULT_ALPHA\nfrom sktime.forecasting.base.adapters import _ProphetAdapter\nfrom sktime.utils.validation._dependencies import _check_soft_dependencies\n\n_check_soft_dependencies(\"prophet\", severity=\"warning\")\n\n\nclass Prophet(_ProphetAdapter):\n    \"\"\"Prophet forecaster by wrapping Facebook's prophet algorithm [1]_.\n\n    Parameters\n    ----------\n    freq: String of DatetimeIndex frequency. Refer [2]_ for possible values:\n    add_seasonality: dict or None, default=None\n        Dict with args for Prophet.add_seasonality().\n        Dict can have the following keys/values:\n            * name: string name of the seasonality component.\n            * period: float number of days in one period.\n            * fourier_order: int number of Fourier components to use.\n            * prior_scale: optional float prior scale for this component.\n            * mode: optional 'additive' or 'multiplicative'\n            * condition_name: string name of the seasonality condition.\n    add_country_holidays: dict or None, default=None\n        Dict with args for Prophet.add_country_holidays().\n        Dict can have the following keys/values:\n            country_name: Name of the country, like 'UnitedStates' or 'US'\n    growth: str, default=\"linear\"\n        String 'linear' or 'logistic' to specify a linear or logistic\n        trend.\n    changepoints: list or None, default=None\n        List of dates at which to include potential changepoints. If\n        not specified, potential changepoints are selected automatically.\n    n_changepoints: int, default=25\n        Number of potential changepoints to include. Not used\n        if input `changepoints` is supplied. If `changepoints` is not supplied,\n        then n_changepoints potential changepoints are selected uniformly from\n        the first `changepoint_range` proportion of the history.\n    changepoint_range: float, default=0.8\n        Proportion of history in which trend changepoints will\n        be estimated. Defaults to 0.8 for the first 80%. Not used if\n        `changepoints` is specified.\n    yearly_seasonality: str or bool or int, default=\"auto\"\n        Fit yearly seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    weekly_seasonality: str or bool or int, default=\"auto\"\n        Fit weekly seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    daily_seasonality: str or bool or int, default=\"auto\"\n        Fit daily seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    holidays: pd.DataFrame or None, default=None\n        pd.DataFrame with columns holiday (string) and ds (date type)\n        and optionally columns lower_window and upper_window which specify a\n        range of days around the date to be included as holidays.\n        lower_window=-2 will include 2 days prior to the date as holidays. Also\n        optionally can have a column prior_scale specifying the prior scale for\n        that holiday.\n    seasonality_mode: str, default='additive'\n        Take one of 'additive' or 'multiplicative'.\n    seasonality_prior_scale: float, default=10.0\n        Parameter modulating the strength of the seasonality model.\n        Larger values allow the model to fit larger seasonal\n        fluctuations, smaller values dampen the seasonality. Can be specified\n        for individual seasonalities using add_seasonality.\n    holidays_prior_scale: float, default=10.0\n        Parameter modulating the strength of the holiday\n        components model, unless overridden in the holidays input.\n    changepoint_prior_scale: float, default=0.05\n        Parameter modulating the flexibility of the\n        automatic changepoint selection. Large values will allow many\n        changepoints, small values will allow few changepoints.\n    mcmc_samples: int, default=0\n        If greater than 0, will do full Bayesian inference\n        with the specified number of MCMC samples. If 0, will do MAP\n        estimation.\n    alpha: float, default=0.05\n        Width of the uncertainty intervals provided\n        for the forecast. If mcmc_samples=0, this will be only the uncertainty\n        in the trend using the MAP estimate of the extrapolated generative\n        model. If mcmc.samples>0, this will be integrated over all model\n        parameters, which will include uncertainty in seasonality.\n    uncertainty_samples: int, default=1000\n        Number of simulated draws used to estimate uncertainty intervals.\n        Settings this value to 0 or False will disable\n        uncertainty estimation and speed up the calculation.\n    stan_backend: str or None, default=None\n        str as defined in StanBackendEnum. If None, will try to\n        iterate over all available backends and find the working one.\n\n    References\n    ----------\n    .. [1] https://facebook.github.io/prophet\n    .. [2] https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n\n    Examples\n    --------\n    >>> from sktime.datasets import load_airline\n    >>> from sktime.forecasting.fbprophet import Prophet\n    >>> # Prophet requires to have data with a pandas.DatetimeIndex\n    >>> y = load_airline().to_timestamp(freq='M')\n    >>> forecaster = Prophet(\n    ...     seasonality_mode='multiplicative',\n    ...     n_changepoints=int(len(y) / 12),\n    ...     add_country_holidays={'country_name': 'Germany'},\n    ...     yearly_seasonality=True)\n    >>> forecaster.fit(y)\n    Prophet(...)\n    >>> y_pred = forecaster.predict(fh=[1,2,3])\n    \"\"\"\n\n    def __init__(\n        self,\n        # Args due to wrapping\n        freq=None,\n        add_seasonality=None,\n        add_country_holidays=None,\n        # Args of fbprophet\n        growth=\"linear\",\n        changepoints=None,\n        n_changepoints=25,\n        changepoint_range=0.8,\n        yearly_seasonality=\"auto\",\n        weekly_seasonality=\"auto\",\n        daily_seasonality=\"auto\",\n        holidays=None,\n        seasonality_mode=\"additive\",\n        seasonality_prior_scale=10.0,\n        holidays_prior_scale=10.0,\n        changepoint_prior_scale=0.05,\n        mcmc_samples=0,\n        alpha=DEFAULT_ALPHA,\n        uncertainty_samples=1000,\n        stan_backend=None,\n        verbose=0,\n        interval_width=0,\n    ):\n        _check_soft_dependencies(\"prophet\", severity=\"error\", object=self)\n\n        self.freq = freq\n        self.add_seasonality = add_seasonality\n        self.add_country_holidays = add_country_holidays\n\n        self.growth = growth\n        self.changepoints = changepoints\n        self.n_changepoints = n_changepoints\n        self.changepoint_range = changepoint_range\n        self.yearly_seasonality = yearly_seasonality\n        self.weekly_seasonality = weekly_seasonality\n        self.daily_seasonality = daily_seasonality\n        self.holidays = holidays\n        self.seasonality_mode = seasonality_mode\n        self.seasonality_prior_scale = seasonality_prior_scale\n        self.changepoint_prior_scale = changepoint_prior_scale\n        self.holidays_prior_scale = holidays_prior_scale\n        self.mcmc_samples = mcmc_samples\n        self.alpha = alpha\n        self.uncertainty_samples = uncertainty_samples\n        self.stan_backend = stan_backend\n        self.verbose = verbose\n        self.interval_width = interval_width\n\n        # import inside method to avoid hard dependency\n        from prophet.forecaster import Prophet as _Prophet\n\n        self._ModelClass = _Prophet\n\n        super(Prophet, self).__init__()\n\n    def _instantiate_model(self):\n        self._forecaster = self._ModelClass(\n            growth=self.growth,\n            changepoints=self.changepoints,\n            n_changepoints=self.n_changepoints,\n            changepoint_range=self.changepoint_range,\n            yearly_seasonality=self.yearly_seasonality,\n            weekly_seasonality=self.weekly_seasonality,\n            daily_seasonality=self.daily_seasonality,\n            holidays=self.holidays,\n            seasonality_mode=self.seasonality_mode,\n            seasonality_prior_scale=float(self.seasonality_prior_scale),\n            holidays_prior_scale=float(self.holidays_prior_scale),\n            changepoint_prior_scale=float(self.changepoint_prior_scale),\n            mcmc_samples=self.mcmc_samples,\n            interval_width=1 - self.alpha,\n            uncertainty_samples=self.uncertainty_samples,\n            stan_backend=self.stan_backend,\n        )\n        return self\n\n    @classmethod\n    def get_test_params(cls):\n        \"\"\"Return testing parameter settings for the estimator.\n\n        Returns\n        -------\n        params : dict or list of dict\n        \"\"\"\n        params = {\n            \"n_changepoints\": 0,\n            \"yearly_seasonality\": False,\n            \"weekly_seasonality\": False,\n            \"daily_seasonality\": False,\n            \"uncertainty_samples\": 1000,\n            \"verbose\": False,\n        }\n        return params\n", "path": "sktime/forecasting/fbprophet.py"}], "after_files": [{"content": "#!/usr/bin/env python3 -u\n# -*- coding: utf-8 -*-\n# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)\n\"\"\"Implements Prophet forecaster by wrapping fbprophet.\"\"\"\n\n__author__ = [\"aiwalter\"]\n__all__ = [\"Prophet\"]\n\n\nfrom sktime.forecasting.base._base import DEFAULT_ALPHA\nfrom sktime.forecasting.base.adapters import _ProphetAdapter\nfrom sktime.utils.validation._dependencies import _check_soft_dependencies\n\n_check_soft_dependencies(\"prophet\", severity=\"warning\")\n\n\nclass Prophet(_ProphetAdapter):\n    \"\"\"Prophet forecaster by wrapping Facebook's prophet algorithm [1]_.\n\n    Parameters\n    ----------\n    freq: str, default=None\n        A DatetimeIndex frequency. For possible values see\n        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n    add_seasonality: dict or None, default=None\n        Dict with args for Prophet.add_seasonality().\n        Dict can have the following keys/values:\n            * name: string name of the seasonality component.\n            * period: float number of days in one period.\n            * fourier_order: int number of Fourier components to use.\n            * prior_scale: optional float prior scale for this component.\n            * mode: optional 'additive' or 'multiplicative'\n            * condition_name: string name of the seasonality condition.\n    add_country_holidays: dict or None, default=None\n        Dict with args for Prophet.add_country_holidays().\n        Dict can have the following keys/values:\n            country_name: Name of the country, like 'UnitedStates' or 'US'\n    growth: str, default=\"linear\"\n        String 'linear' or 'logistic' to specify a linear or logistic\n        trend.\n    changepoints: list or None, default=None\n        List of dates at which to include potential changepoints. If\n        not specified, potential changepoints are selected automatically.\n    n_changepoints: int, default=25\n        Number of potential changepoints to include. Not used\n        if input `changepoints` is supplied. If `changepoints` is not supplied,\n        then n_changepoints potential changepoints are selected uniformly from\n        the first `changepoint_range` proportion of the history.\n    changepoint_range: float, default=0.8\n        Proportion of history in which trend changepoints will\n        be estimated. Defaults to 0.8 for the first 80%. Not used if\n        `changepoints` is specified.\n    yearly_seasonality: str or bool or int, default=\"auto\"\n        Fit yearly seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    weekly_seasonality: str or bool or int, default=\"auto\"\n        Fit weekly seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    daily_seasonality: str or bool or int, default=\"auto\"\n        Fit daily seasonality.\n        Can be 'auto', True, False, or a number of Fourier terms to generate.\n    holidays: pd.DataFrame or None, default=None\n        pd.DataFrame with columns holiday (string) and ds (date type)\n        and optionally columns lower_window and upper_window which specify a\n        range of days around the date to be included as holidays.\n        lower_window=-2 will include 2 days prior to the date as holidays. Also\n        optionally can have a column prior_scale specifying the prior scale for\n        that holiday.\n    seasonality_mode: str, default='additive'\n        Take one of 'additive' or 'multiplicative'.\n    seasonality_prior_scale: float, default=10.0\n        Parameter modulating the strength of the seasonality model.\n        Larger values allow the model to fit larger seasonal\n        fluctuations, smaller values dampen the seasonality. Can be specified\n        for individual seasonalities using add_seasonality.\n    holidays_prior_scale: float, default=10.0\n        Parameter modulating the strength of the holiday\n        components model, unless overridden in the holidays input.\n    changepoint_prior_scale: float, default=0.05\n        Parameter modulating the flexibility of the\n        automatic changepoint selection. Large values will allow many\n        changepoints, small values will allow few changepoints.\n    mcmc_samples: int, default=0\n        If greater than 0, will do full Bayesian inference\n        with the specified number of MCMC samples. If 0, will do MAP\n        estimation.\n    alpha: float, default=0.05\n        Width of the uncertainty intervals provided\n        for the forecast. If mcmc_samples=0, this will be only the uncertainty\n        in the trend using the MAP estimate of the extrapolated generative\n        model. If mcmc.samples>0, this will be integrated over all model\n        parameters, which will include uncertainty in seasonality.\n    uncertainty_samples: int, default=1000\n        Number of simulated draws used to estimate uncertainty intervals.\n        Settings this value to 0 or False will disable\n        uncertainty estimation and speed up the calculation.\n    stan_backend: str or None, default=None\n        str as defined in StanBackendEnum. If None, will try to\n        iterate over all available backends and find the working one.\n\n    References\n    ----------\n    .. [1] https://facebook.github.io/prophet\n\n    Examples\n    --------\n    >>> from sktime.datasets import load_airline\n    >>> from sktime.forecasting.fbprophet import Prophet\n    >>> # Prophet requires to have data with a pandas.DatetimeIndex\n    >>> y = load_airline().to_timestamp(freq='M')\n    >>> forecaster = Prophet(\n    ...     seasonality_mode='multiplicative',\n    ...     n_changepoints=int(len(y) / 12),\n    ...     add_country_holidays={'country_name': 'Germany'},\n    ...     yearly_seasonality=True)\n    >>> forecaster.fit(y)\n    Prophet(...)\n    >>> y_pred = forecaster.predict(fh=[1,2,3])\n    \"\"\"\n\n    def __init__(\n        self,\n        # Args due to wrapping\n        freq=None,\n        add_seasonality=None,\n        add_country_holidays=None,\n        # Args of fbprophet\n        growth=\"linear\",\n        changepoints=None,\n        n_changepoints=25,\n        changepoint_range=0.8,\n        yearly_seasonality=\"auto\",\n        weekly_seasonality=\"auto\",\n        daily_seasonality=\"auto\",\n        holidays=None,\n        seasonality_mode=\"additive\",\n        seasonality_prior_scale=10.0,\n        holidays_prior_scale=10.0,\n        changepoint_prior_scale=0.05,\n        mcmc_samples=0,\n        alpha=DEFAULT_ALPHA,\n        uncertainty_samples=1000,\n        stan_backend=None,\n        verbose=0,\n        interval_width=0,\n    ):\n        _check_soft_dependencies(\"prophet\", severity=\"error\", object=self)\n\n        self.freq = freq\n        self.add_seasonality = add_seasonality\n        self.add_country_holidays = add_country_holidays\n\n        self.growth = growth\n        self.changepoints = changepoints\n        self.n_changepoints = n_changepoints\n        self.changepoint_range = changepoint_range\n        self.yearly_seasonality = yearly_seasonality\n        self.weekly_seasonality = weekly_seasonality\n        self.daily_seasonality = daily_seasonality\n        self.holidays = holidays\n        self.seasonality_mode = seasonality_mode\n        self.seasonality_prior_scale = seasonality_prior_scale\n        self.changepoint_prior_scale = changepoint_prior_scale\n        self.holidays_prior_scale = holidays_prior_scale\n        self.mcmc_samples = mcmc_samples\n        self.alpha = alpha\n        self.uncertainty_samples = uncertainty_samples\n        self.stan_backend = stan_backend\n        self.verbose = verbose\n        self.interval_width = interval_width\n\n        # import inside method to avoid hard dependency\n        from prophet.forecaster import Prophet as _Prophet\n\n        self._ModelClass = _Prophet\n\n        super(Prophet, self).__init__()\n\n    def _instantiate_model(self):\n        self._forecaster = self._ModelClass(\n            growth=self.growth,\n            changepoints=self.changepoints,\n            n_changepoints=self.n_changepoints,\n            changepoint_range=self.changepoint_range,\n            yearly_seasonality=self.yearly_seasonality,\n            weekly_seasonality=self.weekly_seasonality,\n            daily_seasonality=self.daily_seasonality,\n            holidays=self.holidays,\n            seasonality_mode=self.seasonality_mode,\n            seasonality_prior_scale=float(self.seasonality_prior_scale),\n            holidays_prior_scale=float(self.holidays_prior_scale),\n            changepoint_prior_scale=float(self.changepoint_prior_scale),\n            mcmc_samples=self.mcmc_samples,\n            interval_width=1 - self.alpha,\n            uncertainty_samples=self.uncertainty_samples,\n            stan_backend=self.stan_backend,\n        )\n        return self\n\n    @classmethod\n    def get_test_params(cls):\n        \"\"\"Return testing parameter settings for the estimator.\n\n        Returns\n        -------\n        params : dict or list of dict\n        \"\"\"\n        params = {\n            \"n_changepoints\": 0,\n            \"yearly_seasonality\": False,\n            \"weekly_seasonality\": False,\n            \"daily_seasonality\": False,\n            \"uncertainty_samples\": 1000,\n            \"verbose\": False,\n        }\n        return params\n", "path": "sktime/forecasting/fbprophet.py"}]}