def range(
self,
start: Union[IntLike, DateLike, str],
stop: Union[IntLike, DateLike, str],
) -> pd.DataFrame:
if not self._extendable:
raise TypeError(
"""The index in the deterministic process does not \
support extension. Only PeriodIndex, DatetimeIndex with a frequency, \
RangeIndex, and Int64Index that start at 0 and have only unit differences \
can be extended when producing out-of-sample forecasts.
""")
if type(self._index) in (pd.RangeIndex, pd.Int64Index):
start = required_int_like(start, "start")
stop = required_int_like(stop, "stop")
# Add 1 to ensure that the end point is inclusive
stop += 1
return self._range_from_range_index(start, stop)
if isinstance(start, (int, np.integer)):
start = self._int_to_timestamp(start, "start")
else:
start = pd.Timestamp(start)
if isinstance(stop, (int, np.integer)):
stop = self._int_to_timestamp(stop, "stop")
else:
stop = pd.Timestamp(stop)
return self._range_from_time_index(start, stop)
def __init__(
self,
index: Union[Sequence[Hashable], pd.Index],
*,
period: Optional[Union[float, int]] = None,
constant: bool = False,
order: int = 0,
seasonal: bool = False,
fourier: int = 0,
additional_terms: Sequence[DeterministicTerm] = (),
drop: bool = False,
):
if not isinstance(index, pd.Index):
index = pd.Index(index)
self._index = index
self._deterministic_terms: List[DeterministicTerm] = []
self._extendable = False
self._index_freq = None
self._validate_index()
period = float_like(period, "period", optional=True)
self._constant = constant = bool_like(constant, "constant")
self._order = required_int_like(order, "order")
self._seasonal = seasonal = bool_like(seasonal, "seasonal")
self._fourier = required_int_like(fourier, "fourier")
additional_terms = tuple(additional_terms)
self._cached_in_sample = None
self._drop = bool_like(drop, "drop")
self._additional_terms = additional_terms
if constant or order:
self._deterministic_terms.append(TimeTrend(constant, order))
if seasonal and fourier:
raise ValueError(
"""seasonal and fourier can be initialized through the constructor since\
these will be necessarily perfectly collinear. Instead, you can pass \
additional components using the additional_terms input.""")
if (seasonal or fourier) and period is None:
if period is None:
self._period = period = freq_to_period(self._index_freq)
if seasonal:
period = required_int_like(period, "period")
self._deterministic_terms.append(Seasonality(period))
elif fourier:
period = float_like(period, "period")
assert period is not None
self._deterministic_terms.append(Fourier(period, order=fourier))
for term in additional_terms:
if not isinstance(term, DeterministicTerm):
raise TypeError(
"All additional terms must be instances of subsclasses "
"of DeterministicTerm")
if term not in self._deterministic_terms:
self._deterministic_terms.append(term)
else:
raise ValueError(
"One or more terms in additional_terms has been added "
"through the parameters of the constructor. Terms must "
"be unique.")
self._period = period
self._retain_cols: Optional[List[Hashable]] = None
def __init__(self, period: int, initial_period: int = 1) -> None:
self._period = required_int_like(period, "period")
self._initial_period = required_int_like(initial_period,
"initial_period")
if period < 2:
raise ValueError("period must be >= 2")
if not 1 <= self._initial_period <= period:
raise ValueError("initial_period must be in {1, 2, ..., period}")
def out_of_sample(
self,
steps: int,
forecast_index: Optional[Union[Sequence[Hashable], pd.Index]] = None,
) -> pd.DataFrame:
steps = required_int_like(steps, "steps")
if self._drop and self._retain_cols is None:
self.in_sample()
index = self._index
if not self._deterministic_terms:
return pd.DataFrame(np.empty((index.shape[0], 0)), index=index)
raw_terms = []
for term in self._deterministic_terms:
raw_terms.append(term.out_of_sample(steps, index, forecast_index))
terms: pd.DataFrame = pd.concat(raw_terms, axis=1)
assert self._retain_cols is not None
if terms.shape[1] != len(self._retain_cols):
terms = terms[self._retain_cols]
return terms
def __init__(self, freq: str, order: int) -> None:
super().__init__(freq)
FourierDeterministic.__init__(self, order)
self._order = required_int_like(order, "terms")
def __init__(self, order: int) -> None:
self._order = required_int_like(order, "terms")
def __init__(self, constant: bool = True, order: int = 0) -> None:
self._constant = bool_like(constant, "constant")
self._order = required_int_like(order, "order")
