def predict(self, dates):
if self.solution is None:
raise ValueError('Solution is not present, must first call fit')
# set the result to zero by default, points before the
# first data point will remain zero
result = np.zeros(len(dates))
x = np.array([scale_date(date, self.timescale) for date in dates])
# do the interpolation for the internal region
interpolate_region = (x >= self.x_min) & (x <= self.x_max)
result[interpolate_region] = self.interpolate(x[interpolate_region])
# do the extrapolation beyond the last data point
extrapolate_region = x > self.x_max
x_extrap = x[extrapolate_region]
dates_extrap = np.array(dates)[extrapolate_region]
non_seasonal_extrap = np.array([self.extrapolate_without_seasonal(xx)
for xx in x_extrap])
seasonal_extrap = np.array([self.date_to_seasonal_component(d) for d in dates_extrap])
extrapolated = non_seasonal_extrap + seasonal_extrap
result[extrapolate_region] = extrapolated
return result
def __init__(self, dates, y,
timescale=None,
seasonality_function=None,
params=None):
"""
Instantiate and initialize the object
:param dates: iterable of datetime.date or datetime.datetime objects
:param y: iterable, the y-values for the time series
:param timescale: sets the time scale for scaling dates to numbers
allows the regularization parameters to stay scale
independent
:param seasonality_function: a function that maps dates to an index
pertaining to seasonality variables (zero indexed)
example: lambda date: date.month-1
:param params: a dictionary of overrides for default parameters,
mostly regularization parameters, see default_params
:return: an object instance
"""
if timescale is None:
# timescale = (1, 'month')
timescale = default_timescale(dates)
self.dates = np.array(dates)
self.y = np.array(y)
# scale the dates to an index
self.timescale = timescale
self.x = np.array([scale_date(date, self.timescale) for date in dates])
self.x_min = min(self.x)
self.x_max = max(self.x)
self.params = default_params()
if seasonality_function is None:
# does it make sense to have a default?
# only with seasonality turned off
seasonality_function = lambda the_date: the_date.month - 1
# some largish number
self.params['beta_seasonal'] = 13796.0
self.seasonality_function = seasonality_function
# calculate the seasonality matrix, this combined with the
# index 'x' allows us to forget about dates and call the
# date agnostic fit function
mat, compress = get_seasonality_matrix(dates, seasonality_function)
self.seasonality_matrix = mat
self.compression_dict = compress
self.solution = None
self.slope = None
self.offset = None
self.seasonal = None
# dummy functions for now, replace after fit
self.interpolate = lambda x: None
self.extrapolate_without_seasonal = lambda x: None
self.date_to_seasonal_component = lambda x: None
if params is not None:
# override any parameters handed in with
# params dictionary
for key, value in params.items():
self.params[key] = value