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Functional daily seasonality (#239)
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Ben Letham
GitHub
parent
f1ef4cc190
commit
825108b226
1 changed files with 44 additions and 19 deletions
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@ -50,8 +50,12 @@ class Prophet(object):
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if input `changepoints` is supplied. If `changepoints` is not supplied,
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then n_changepoints potential changepoints are selected uniformly from
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the first 80 percent of the history.
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yearly_seasonality: Fit yearly seasonality. Can be 'auto', True, or False.
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weekly_seasonality: Fit weekly seasonality. Can be 'auto', True, or False.
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yearly_seasonality: Fit yearly seasonality.
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Can be 'auto', True, False, or a number of Fourier terms to generate.
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weekly_seasonality: Fit weekly seasonality.
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Can be 'auto', True, False, or a number of Fourier terms to generate.
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daily_seasonality: Fit daily seasonality.
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Can be 'auto', True, False, or a number of Fourier terms to generate.
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holidays: pd.DataFrame with columns holiday (string) and ds (date type)
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and optionally columns lower_window and upper_window which specify a
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range of days around the date to be included as holidays.
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@ -84,6 +88,7 @@ class Prophet(object):
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n_changepoints=25,
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yearly_seasonality='auto',
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weekly_seasonality='auto',
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daily_seasonality='auto',
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holidays=None,
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seasonality_prior_scale=10.0,
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holidays_prior_scale=10.0,
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@ -154,8 +159,8 @@ class Prophet(object):
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for h in self.holidays['holiday'].unique():
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if '_delim_' in h:
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raise ValueError('Holiday name cannot contain "_delim_"')
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if h in ['zeros', 'yearly', 'weekly', 'yhat', 'seasonal',
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'trend']:
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if h in ['zeros', 'yearly', 'weekly', 'daily', 'yhat',
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'seasonal', 'trend']:
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raise ValueError('Holiday name {} reserved.'.format(h))
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def setup_dataframe(self, df, initialize_scales=False):
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@ -259,8 +264,9 @@ 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.total_seconds()/(24*3600)
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)
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.dt.total_seconds()
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.astype(np.float)
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) / (3600 * 24.)
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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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for i in range(series_order)
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@ -354,28 +360,28 @@ class Prophet(object):
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]
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# Seasonality features
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if self.yearly_seasonality:
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if self.yearly_seasonality > 0:
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seasonal_features.append(self.make_seasonality_features(
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df['ds'],
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365.25,
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10,
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self.yearly_seasonality,
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'yearly',
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))
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if self.weekly_seasonality:
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if self.weekly_seasonality > 0:
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seasonal_features.append(self.make_seasonality_features(
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df['ds'],
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7,
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3,
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self.weekly_seasonality,
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'weekly',
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))
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if self.daily_seasonality:
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if self.daily_seasonality > 0:
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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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self.daily_seasonality,
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'daily',
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))
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if self.holidays is not None:
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@ -393,21 +399,40 @@ class Prophet(object):
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last = self.history['ds'].max()
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if self.yearly_seasonality == 'auto':
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if last - first < pd.Timedelta(days=730):
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self.yearly_seasonality = False
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self.yearly_seasonality = 0
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logger.info('Disabling yearly seasonality. Run prophet with '
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'yearly_seasonality=True to override this.')
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'yearly_seasonality=True to override this.')
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else:
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self.yearly_seasonality = True
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self.yearly_seasonality = 10
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elif self.yearly_seasonality is True:
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self.yearly_seasonality = 10
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if self.weekly_seasonality == 'auto':
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dt = self.history['ds'].diff()
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min_dt = dt.iloc[dt.nonzero()[0]].min()
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if ((last - first < pd.Timedelta(weeks=2)) or
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(min_dt >= pd.Timedelta(weeks=1))):
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self.weekly_seasonality = False
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self.weekly_seasonality = 0
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logger.info('Disabling weekly seasonality. Run prophet with '
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'weekly_seasonality=True to override this.')
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'weekly_seasonality=True to override this.')
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else:
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self.weekly_seasonality = True
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self.weekly_seasonality = 3
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elif self.weekly_seasonality is True:
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self.weekly_seasonality = 3
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if self.daily_seasonality == 'auto':
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# disabled by default but if the average difference is <1 day
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# then we assume there is intra-day modeling
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dt = self.history['ds'].diff()
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min_dt = dt.iloc[dt.nonzero()[0]].min()
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if (min_dt< pd.Timedelta(days=1)):
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self.daily_seasonality = 4
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logger.info('Enabling daily seasonality. Run prophet with '
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'daily_seasonality=False to override this.')
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else:
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self.daily_seasonality = 0
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elif self.daily_seasonality is True:
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self.daily_seasonality = 4
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@staticmethod
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def linear_growth_init(df):
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