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@@ -74,6 +74,7 @@ class Prophet(object):
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parameters, which will include uncertainty in seasonality.
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uncertainty_samples: Number of simulated draws used to estimate
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uncertainty intervals.
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+ daily_seasonality: Boolean, fit daily seasonality
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"""
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def __init__(
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@@ -90,6 +91,7 @@ class Prophet(object):
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mcmc_samples=0,
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interval_width=0.80,
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uncertainty_samples=1000,
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+ daily_seasonality=False,
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):
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self.growth = growth
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@@ -101,6 +103,7 @@ class Prophet(object):
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self.yearly_seasonality = yearly_seasonality
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self.weekly_seasonality = weekly_seasonality
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+ self.daily_seasonality = daily_seasonality
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if holidays is not None:
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if not (
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@@ -256,8 +259,7 @@ class Prophet(object):
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# convert to days since epoch
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t = np.array(
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(dates - pd.datetime(1970, 1, 1))
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- .dt.days
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- .astype(np.float)
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+ .dt.total_seconds()/(24*3600)
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)
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return np.column_stack([
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fun((2.0 * (i + 1) * np.pi * t / period))
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@@ -368,6 +370,14 @@ class Prophet(object):
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'weekly',
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))
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+ if self.daily_seasonality:
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+ seasonal_features.append(self.make_seasonality_features(
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+ df['ds'],
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+ 1,
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+ 3,
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+ 'daily'
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+ ))
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+
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if self.holidays is not None:
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seasonal_features.append(self.make_holiday_features(df['ds']))
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return pd.concat(seasonal_features, axis=1)
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