def pmd_univariate(y: Y_TYPE,
s: dict,
k: int = 1,
a: A_TYPE = None,
t: T_TYPE = None,
e: E_TYPE = None):
""" Uses only y[0] and ignores y[1:] and a[:] """
y0 = [wrap(y)[0]]
return pmd_skater_factory(y=y0, s=s, k=k, a=None, t=t, e=e, method='auto')
def pmd_known(y: Y_TYPE,
s: dict,
k: int = 1,
a: A_TYPE = None,
t: T_TYPE = None,
e: E_TYPE = None):
""" Uses known-in-advance but not y[1:] """
y0 = [wrap(y)[0]]
return pmd_skater_factory(y=y0, s=s, k=k, a=a, t=t, e=e, method='auto')
def pmd_exogenous(y: Y_TYPE,
s: dict,
k: int = 1,
a: A_TYPE = None,
t: T_TYPE = None,
e: E_TYPE = None):
""" Predict using auto_arima, with both simultaneously observed and known in advance variables
This skater has no hyper-parameters
y: Y_TYPE scalar or list where y[1:] are interpreted as contemporaneously observed exogenous variables
s: state
k: Number of steps ahead to predict
a: (optional) scalar or list of variables known k-steps in advance.
(IMPORTANT: If supplying 'a', provide the known variable k steps ahead, not the contemporaneous one !).
t: (optional) Time of observation.
e: (optional) Maximum computation time (supply e>60 to give hint to do fitting)
:returns: x [float] , s', scale [float]
"""
return pmd_skater_factory(y=y, s=s, k=k, a=a, t=t, e=e, method='auto')