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Minor docstring updates

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This commit is contained in:
Ben Letham 2020-08-18 13:04:39 -07:00
parent 0d0f508cf1
commit fb82a6a43d
2 changed files with 14 additions and 8 deletions
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14
python/fbprophet/diagnostics.py
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@ -62,7 +62,7 @@ def cross_validation(model, horizon, period=None, initial=None, parallel=None, c
"""Cross-Validation for time series.
Computes forecasts from historical cutoff points, which user can input.
If not provided beginning from (end - horizon), works backwards making
If not provided, begins from (end - horizon) and works backwards, making
cutoffs with a spacing of period until initial is reached.
When period is equal to the time interval of the data, this is the
@ -70,17 +70,17 @@ def cross_validation(model, horizon, period=None, initial=None, parallel=None, c
Parameters
----------
model: Prophet class object. Fitted Prophet model
model: Prophet class object. Fitted Prophet model.
horizon: string with pd.Timedelta compatible style, e.g., '5 days',
'3 hours', '10 seconds'.
period: string with pd.Timedelta compatible style. Simulated forecast will
be done at every this period. If not provided, 0.5 * horizon is used.
initial: string with pd.Timedelta compatible style. The first training
period will begin here. If not provided, 3 * horizon is used.
cutoffs: list of pd.Timestamp representing cutoff to be used during
cross-validtation. If not provided works beginning from
(end - horizon), works backwards making cutoffs with a spacing of period
until initial is reached.
period will include at least this much data. If not provided,
3 * horizon is used.
cutoffs: list of pd.Timestamp specifying cutoffs to be used during
cross validtation. If not provided, they are generated as described
above.
parallel : {None, 'processes', 'threads', 'dask', object}
How to parallelize the forecast computation. By default no parallelism

8
python/fbprophet/forecaster.py
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@ -1399,7 +1399,13 @@ class Prophet(object):
return sim_values
def predictive_samples(self, df):
"""Sample from the posterior predictive distribution.
"""Sample from the posterior predictive distribution. Returns samples
for the main estimate yhat, and for the trend component. The shape of
each output will be (nforecast x nsamples), where nforecast is the
number of points being forecasted (the number of rows in the input
dataframe) and nsamples is the number of posterior samples drawn.
This is the argument `uncertainty_samples` in the Prophet constructor,
which defaults to 1000.
Parameters
----------