Source code for statsmodels.tsa.deterministic

from statsmodels.compat.pandas import (
    Appender,
    NumericIndex,
    is_int_index,
    to_numpy,
)

from abc import ABC, abstractmethod
import datetime as dt
from typing import Hashable, List, Optional, Sequence, Set, Tuple, Type, Union

import numpy as np
import pandas as pd
from scipy.linalg import qr

from statsmodels.iolib.summary import d_or_f
from statsmodels.tools.validation import (
    bool_like,
    float_like,
    required_int_like,
    string_like,
)
from statsmodels.tsa.tsatools import freq_to_period

DateLike = Union[dt.datetime, pd.Timestamp, np.datetime64]
IntLike = Union[int, np.integer]


START_BEFORE_INDEX_ERR = """\
start is less than the first observation in the index. Values can only be \
created for observations after the start of the index.
"""


[docs]class DeterministicTerm(ABC): """Abstract Base Class for all Deterministic Terms""" # Set _is_dummy if the term is a dummy variable process _is_dummy = False @property def is_dummy(self) -> bool: """Flag indicating whether the values produced are dummy variables""" return self._is_dummy
[docs] @abstractmethod def in_sample(self, index: Sequence[Hashable]) -> pd.DataFrame: """ Produce deterministic trends for in-sample fitting. Parameters ---------- index : index_like An index-like object. If not an index, it is converted to an index. Returns ------- DataFrame A DataFrame containing the deterministic terms. """
[docs] @abstractmethod def out_of_sample( self, steps: int, index: Sequence[Hashable], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: """ Produce deterministic trends for out-of-sample forecasts Parameters ---------- steps : int The number of steps to forecast index : index_like An index-like object. If not an index, it is converted to an index. forecast_index : index_like An Index or index-like object to use for the forecasts. If provided must have steps elements. Returns ------- DataFrame A DataFrame containing the deterministic terms. """
@abstractmethod def __str__(self) -> str: """A meaningful string representation of the term""" def __hash__(self) -> int: name: Tuple[Hashable, ...] = (type(self).__name__,) return hash(name + self._eq_attr) @property @abstractmethod def _eq_attr(self) -> Tuple[Hashable, ...]: """tuple of attributes that are used for equality comparison""" @staticmethod def _index_like(index: Sequence[Hashable]) -> pd.Index: if isinstance(index, pd.Index): return index try: return pd.Index(index) except Exception: raise TypeError("index must be a pandas Index or index-like") @staticmethod def _extend_index( index: pd.Index, steps: int, forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.Index: """Extend the forecast index""" if forecast_index is not None: forecast_index = DeterministicTerm._index_like(forecast_index) assert isinstance(forecast_index, pd.Index) if forecast_index.shape[0] != steps: raise ValueError( f"The number of values in forecast_index " f"({forecast_index.shape[0]}) must match steps ({steps})." ) return forecast_index if isinstance(index, pd.PeriodIndex): return pd.period_range( index[-1] + 1, periods=steps, freq=index.freq ) elif isinstance(index, pd.DatetimeIndex) and index.freq is not None: next_obs = pd.date_range(index[-1], freq=index.freq, periods=2)[1] return pd.date_range(next_obs, freq=index.freq, periods=steps) elif isinstance(index, pd.RangeIndex): assert isinstance(index, pd.RangeIndex) try: step = index.step start = index.stop except AttributeError: # TODO: Remove after pandas min ver is 1.0.0+ step = index[-1] - index[-2] if len(index) > 1 else 1 start = index[-1] + step stop = start + step * steps return pd.RangeIndex(start, stop, step=step) elif is_int_index(index) and np.all(np.diff(index) == 1): idx_arr = np.arange(index[-1] + 1, index[-1] + steps + 1) return NumericIndex(idx_arr) # default range index import warnings warnings.warn( "Only PeriodIndexes, DatetimeIndexes with a frequency set, " "RangesIndexes, and NumericIndex with a unit increment support " "extending. The index is set will contain the position relative " "to the data length.", UserWarning, ) nobs = index.shape[0] return pd.RangeIndex(nobs + 1, nobs + steps + 1) def __repr__(self) -> str: return self.__str__() + f" at 0x{id(self):0x}" def __eq__(self, other: object) -> bool: if isinstance(other, type(self)): own_attr = self._eq_attr oth_attr = other._eq_attr if len(own_attr) != len(oth_attr): return False return all([a == b for a, b in zip(own_attr, oth_attr)]) else: return False
[docs]class TimeTrendDeterministicTerm(DeterministicTerm, ABC): """Abstract Base Class for all Time Trend Deterministic Terms""" def __init__(self, constant: bool = True, order: int = 0) -> None: self._constant = bool_like(constant, "constant") self._order = required_int_like(order, "order") @property def constant(self) -> bool: """Flag indicating that a constant is included""" return self._constant @property def order(self) -> int: """Order of the time trend""" return self._order @property def _columns(self) -> List[str]: columns = [] trend_names = {1: "trend", 2: "trend_squared", 3: "trend_cubed"} if self._constant: columns.append("const") for power in range(1, self._order + 1): if power in trend_names: columns.append(trend_names[power]) else: columns.append(f"trend**{power}") return columns def _get_terms(self, locs: np.ndarray) -> np.ndarray: nterms = int(self._constant) + self._order terms = np.tile(locs, (1, nterms)) power = np.zeros((1, nterms), dtype=int) power[0, int(self._constant) :] = np.arange(1, self._order + 1) terms **= power return terms def __str__(self) -> str: terms = [] if self._constant: terms.append("Constant") if self._order: terms.append(f"Powers 1 to {self._order + 1}") if not terms: terms = ["Empty"] terms_str = ",".join(terms) return f"TimeTrend({terms_str})"
[docs]class TimeTrend(TimeTrendDeterministicTerm): """ Constant and time trend determinstic terms Parameters ---------- constant : bool Flag indicating whether a constant should be included. order : int A non-negative int containing the powers to include (1, 2, ..., order). See Also -------- DeterministicProcess Seasonality Fourier CalendarTimeTrend Examples -------- >>> from statsmodels.datasets import sunspots >>> from statsmodels.tsa.deterministic import TimeTrend >>> data = sunspots.load_pandas().data >>> trend_gen = TimeTrend(True, 3) >>> trend_gen.in_sample(data.index) """ def __init__(self, constant: bool = True, order: int = 0) -> None: super().__init__(constant, order)
[docs] @classmethod def from_string(cls, trend: str) -> "TimeTrend": """ Create a TimeTrend from a string description. Provided for compatibility with common string names. Parameters ---------- trend : {"n", "c", "t", "ct", "ctt"} The string representation of the time trend. The terms are: * "n": No trend terms * "c": A constant only * "t": Linear time trend only * "ct": A constant and a time trend * "ctt": A constant, a time trend and a quadratic time trend Returns ------- TimeTrend The TimeTrend instance. """ constant = trend.startswith("c") order = 0 if "tt" in trend: order = 2 elif "t" in trend: order = 1 return cls(constant=constant, order=order)
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) nobs = index.shape[0] locs = np.arange(1, nobs + 1, dtype=np.double)[:, None] terms = self._get_terms(locs) return pd.DataFrame(terms, columns=self._columns, index=index)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) nobs = index.shape[0] fcast_index = self._extend_index(index, steps, forecast_index) locs = np.arange(nobs + 1, nobs + steps + 1, dtype=np.double)[:, None] terms = self._get_terms(locs) return pd.DataFrame(terms, columns=self._columns, index=fcast_index)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: return self._constant, self._order
[docs]class Seasonality(DeterministicTerm): """ Seasonal dummy deterministic terms Parameters ---------- period : int The length of a full cycle. Must be >= 2. initial_period : int The seasonal index of the first observation. 1-indexed so must be in {1, 2, ..., period}. See Also -------- DeterministicProcess TimeTrend Fourier CalendarSeasonality Examples -------- Solar data has an 11-year cycle >>> from statsmodels.datasets import sunspots >>> from statsmodels.tsa.deterministic import Seasonality >>> data = sunspots.load_pandas().data >>> seas_gen = Seasonality(11) >>> seas_gen.in_sample(data.index) To start at a season other than 1 >>> seas_gen = Seasonality(11, initial_period=4) >>> seas_gen.in_sample(data.index) """ _is_dummy = True 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}") @property def period(self) -> int: """The period of the seasonality""" return self._period @property def initial_period(self) -> int: """The seasonal index of the first observation""" return self._initial_period
[docs] @classmethod def from_index( cls, index: Union[Sequence[Hashable], pd.DatetimeIndex, pd.PeriodIndex] ) -> "Seasonality": """ Construct a seasonality directly from an index using its frequency. Parameters ---------- index : {DatetimeIndex, PeriodIndex} An index with its frequency (`freq`) set. Returns ------- Seasonality The initialized Seasonality instance. """ index = cls._index_like(index) if isinstance(index, pd.PeriodIndex): freq = index.freq elif isinstance(index, pd.DatetimeIndex): freq = index.freq if index.freq else index.inferred_freq else: raise TypeError("index must be a DatetimeIndex or PeriodIndex") if freq is None: raise ValueError("index must have a freq or inferred_freq set") period = freq_to_period(freq) return cls(period=period)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: return self._period, self._initial_period def __str__(self) -> str: return f"Seasonality(period={self._period})" @property def _columns(self) -> List[str]: period = self._period columns = [] for i in range(1, period + 1): columns.append(f"s({i},{period})") return columns
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) nobs = index.shape[0] period = self._period term = np.zeros((nobs, period)) offset = self._initial_period - 1 for i in range(period): col = (i + offset) % period term[i::period, col] = 1 return pd.DataFrame(term, columns=self._columns, index=index)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) fcast_index = self._extend_index(index, steps, forecast_index) nobs = index.shape[0] period = self._period term = np.zeros((steps, period)) offset = self._initial_period - 1 for i in range(period): col_loc = (nobs + offset + i) % period term[i::period, col_loc] = 1 return pd.DataFrame(term, columns=self._columns, index=fcast_index)
[docs]class FourierDeterministicTerm(DeterministicTerm, ABC): """Abstract Base Class for all Fourier Deterministic Terms""" def __init__(self, order: int) -> None: self._order = required_int_like(order, "terms") @property def order(self) -> int: """The order of the Fourier terms included""" return self._order def _get_terms(self, locs: np.ndarray) -> np.ndarray: locs = 2 * np.pi * locs.astype(np.double) terms = np.empty((locs.shape[0], 2 * self._order)) for i in range(self._order): for j, func in enumerate((np.sin, np.cos)): terms[:, 2 * i + j] = func((i + 1) * locs) return terms
[docs]class Fourier(FourierDeterministicTerm): r""" Fourier series deterministic terms Parameters ---------- period : int The length of a full cycle. Must be >= 2. order : int The number of Fourier components to include. Must be <= 2*period. See Also -------- DeterministicProcess TimeTrend Seasonality CalendarFourier Notes ----- Both a sine and a cosine term are included for each i=1, ..., order .. math:: f_{i,s,t} & = \sin\left(2 \pi i \times \frac{t}{m} \right) \\ f_{i,c,t} & = \cos\left(2 \pi i \times \frac{t}{m} \right) where m is the length of the period. Examples -------- Solar data has an 11-year cycle >>> from statsmodels.datasets import sunspots >>> from statsmodels.tsa.deterministic import Fourier >>> data = sunspots.load_pandas().data >>> fourier_gen = Fourier(11, order=2) >>> fourier_gen.in_sample(data.index) """ _is_dummy = False def __init__(self, period: float, order: int): super().__init__(order) self._period = float_like(period, "period") if 2 * self._order > self._period: raise ValueError("2 * order must be <= period") @property def period(self) -> float: """The period of the Fourier terms""" return self._period @property def _columns(self) -> List[str]: period = self._period fmt_period = d_or_f(period).strip() columns = [] for i in range(1, self._order + 1): for typ in ("sin", "cos"): columns.append(f"{typ}({i},{fmt_period})") return columns
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) nobs = index.shape[0] terms = self._get_terms(np.arange(nobs) / self._period) return pd.DataFrame(terms, index=index, columns=self._columns)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) fcast_index = self._extend_index(index, steps, forecast_index) nobs = index.shape[0] terms = self._get_terms(np.arange(nobs, nobs + steps) / self._period) return pd.DataFrame(terms, index=fcast_index, columns=self._columns)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: return self._period, self._order def __str__(self) -> str: return f"Fourier(period={self._period}, order={self._order})"
[docs]class CalendarDeterministicTerm(DeterministicTerm, ABC): """Abstract Base Class for calendar deterministic terms""" def __init__(self, freq: str) -> None: try: index = pd.date_range("2020-01-01", freq=freq, periods=1) self._freq = index.freq except ValueError: raise ValueError("freq is not understood by pandas") @property def freq(self) -> str: """The frequency of the deterministic terms""" return self._freq.freqstr def _compute_ratio( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: if isinstance(index, pd.PeriodIndex): index = index.to_timestamp() delta = index - index.to_period(self._freq).to_timestamp() pi = index.to_period(self._freq) gap = (pi + 1).to_timestamp() - pi.to_timestamp() return to_numpy(delta) / to_numpy(gap) def _check_index_type( self, index: pd.Index, allowed: Union[Type, Tuple[Type, ...]] = ( pd.DatetimeIndex, pd.PeriodIndex, ), ) -> Union[pd.DatetimeIndex, pd.PeriodIndex]: if isinstance(allowed, type): allowed = (allowed,) if not isinstance(index, allowed): if len(allowed) == 1: allowed_types = "a " + allowed[0].__name__ else: allowed_types = ", ".join(a.__name__ for a in allowed[:-1]) if len(allowed) > 2: allowed_types += "," allowed_types += " and " + allowed[-1].__name__ msg = ( f"{type(self).__name__} terms can only be computed from " f"{allowed_types}" ) raise TypeError(msg) assert isinstance(index, (pd.DatetimeIndex, pd.PeriodIndex)) return index
[docs]class CalendarFourier(CalendarDeterministicTerm, FourierDeterministicTerm): r""" Fourier series deterministic terms based on calendar time Parameters ---------- freq : str A string convertible to a pandas frequency. order : int The number of Fourier components to include. Must be <= 2*period. See Also -------- DeterministicProcess CalendarTimeTrend CalendarSeasonality Fourier Notes ----- Both a sine and a cosine term are included for each i=1, ..., order .. math:: f_{i,s,t} & = \sin\left(2 \pi i \tau_t \right) \\ f_{i,c,t} & = \cos\left(2 \pi i \tau_t \right) where m is the length of the period and :math:`\tau_t` is the frequency normalized time. For example, when freq is "D" then an observation with a timestamp of 12:00:00 would have :math:`\tau_t=0.5`. Examples -------- Here we simulate irregularly spaced hourly data and construct the calendar Fourier terms for the data. >>> import numpy as np >>> import pandas as pd >>> base = pd.Timestamp("2020-1-1") >>> gen = np.random.default_rng() >>> gaps = np.cumsum(gen.integers(0, 1800, size=1000)) >>> times = [base + pd.Timedelta(gap, unit="s") for gap in gaps] >>> index = pd.DatetimeIndex(pd.to_datetime(times)) >>> from statsmodels.tsa.deterministic import CalendarFourier >>> cal_fourier_gen = CalendarFourier("D", 2) >>> cal_fourier_gen.in_sample(index) """ def __init__(self, freq: str, order: int) -> None: super().__init__(freq) FourierDeterministicTerm.__init__(self, order) self._order = required_int_like(order, "terms") @property def _columns(self) -> List[str]: columns = [] for i in range(1, self._order + 1): for typ in ("sin", "cos"): columns.append(f"{typ}({i},freq={self._freq.freqstr})") return columns
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) index = self._check_index_type(index) ratio = self._compute_ratio(index) terms = self._get_terms(ratio) return pd.DataFrame(terms, index=index, columns=self._columns)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) fcast_index = self._extend_index(index, steps, forecast_index) self._check_index_type(fcast_index) assert isinstance(fcast_index, (pd.DatetimeIndex, pd.PeriodIndex)) ratio = self._compute_ratio(fcast_index) terms = self._get_terms(ratio) return pd.DataFrame(terms, index=fcast_index, columns=self._columns)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: return self._freq.freqstr, self._order def __str__(self) -> str: return f"Fourier(freq={self._freq.freqstr}, order={self._order})"
[docs]class CalendarSeasonality(CalendarDeterministicTerm): """ Seasonal dummy deterministic terms based on calendar time Parameters ---------- freq : str The frequency of the seasonal effect. period : str The pandas frequency string describing the full period. See Also -------- DeterministicProcess CalendarTimeTrend CalendarFourier Seasonality Examples -------- Here we simulate irregularly spaced data (in time) and hourly seasonal dummies for the data. >>> import numpy as np >>> import pandas as pd >>> base = pd.Timestamp("2020-1-1") >>> gen = np.random.default_rng() >>> gaps = np.cumsum(gen.integers(0, 1800, size=1000)) >>> times = [base + pd.Timedelta(gap, unit="s") for gap in gaps] >>> index = pd.DatetimeIndex(pd.to_datetime(times)) >>> from statsmodels.tsa.deterministic import CalendarSeasonality >>> cal_seas_gen = CalendarSeasonality("H", "D") >>> cal_seas_gen.in_sample(index) """ _is_dummy = True # out_of: freq _supported = { "W": {"H": 24 * 7, "B": 5, "D": 7}, "D": {"H": 24}, "Q": {"M": 3}, "A": {"M": 12, "Q": 4}, } def __init__(self, freq: str, period: str) -> None: freq_options: Set[str] = set() freq_options.update( *[list(val.keys()) for val in self._supported.values()] ) period_options = list(self._supported.keys()) freq = string_like( freq, "freq", options=tuple(freq_options), lower=False ) period = string_like( period, "period", options=period_options, lower=False ) if freq not in self._supported[period]: raise ValueError( f"The combination of freq={freq} and " f"period={period} is not supported." ) super().__init__(freq) self._period = period self._freq_str = self._freq.freqstr.split("-")[0] @property def freq(self) -> str: """The frequency of the deterministic terms""" return self._freq.freqstr @property def period(self) -> str: """The full period""" return self._period def _weekly_to_loc( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: if self._freq.freqstr == "H": return index.hour + 24 * index.dayofweek elif self._freq.freqstr == "D": return index.dayofweek else: # "B" bdays = pd.bdate_range("2000-1-1", periods=10).dayofweek.unique() loc = index.dayofweek if not loc.isin(bdays).all(): raise ValueError( "freq is B but index contains days that are not business " "days." ) return loc def _daily_to_loc( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: return index.hour def _quarterly_to_loc( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: return (index.month - 1) % 3 def _annual_to_loc( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: if self._freq.freqstr == "M": return index.month - 1 else: # "Q" return index.quarter - 1 def _get_terms( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex] ) -> np.ndarray: if self._period == "D": locs = self._daily_to_loc(index) elif self._period == "W": locs = self._weekly_to_loc(index) elif self._period == "Q": locs = self._quarterly_to_loc(index) else: # "A": locs = self._annual_to_loc(index) full_cycle = self._supported[self._period][self._freq_str] terms = np.zeros((locs.shape[0], full_cycle)) terms[np.arange(locs.shape[0]), locs] = 1 return terms @property def _columns(self) -> List[str]: columns = [] count = self._supported[self._period][self._freq_str] for i in range(count): columns.append( f"s({self._freq_str}={i + 1}, period={self._period})" ) return columns
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) index = self._check_index_type(index) terms = self._get_terms(index) return pd.DataFrame(terms, index=index, columns=self._columns)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) fcast_index = self._extend_index(index, steps, forecast_index) self._check_index_type(fcast_index) assert isinstance(fcast_index, (pd.DatetimeIndex, pd.PeriodIndex)) terms = self._get_terms(fcast_index) return pd.DataFrame(terms, index=fcast_index, columns=self._columns)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: return self._period, self._freq_str def __str__(self) -> str: return f"Seasonal(freq={self._freq_str})"
[docs]class CalendarTimeTrend(CalendarDeterministicTerm, TimeTrendDeterministicTerm): r""" Constant and time trend determinstic terms based on calendar time Parameters ---------- freq : str A string convertible to a pandas frequency. constant : bool Flag indicating whether a constant should be included. order : int A non-negative int containing the powers to include (1, 2, ..., order). base_period : {str, pd.Timestamp}, default None The base period to use when computing the time stamps. This value is treated as 1 and so all other time indices are defined as the number of periods since or before this time stamp. If not provided, defaults to pandas base period for a PeriodIndex. See Also -------- DeterministicProcess CalendarFourier CalendarSeasonality TimeTrend Notes ----- The time stamp, :math:`\tau_t`, is the number of periods that have elapsed since the base_period. :math:`\tau_t` may be fractional. Examples -------- Here we simulate irregularly spaced hourly data and construct the calendar time trend terms for the data. >>> import numpy as np >>> import pandas as pd >>> base = pd.Timestamp("2020-1-1") >>> gen = np.random.default_rng() >>> gaps = np.cumsum(gen.integers(0, 1800, size=1000)) >>> times = [base + pd.Timedelta(gap, unit="s") for gap in gaps] >>> index = pd.DatetimeIndex(pd.to_datetime(times)) >>> from statsmodels.tsa.deterministic import CalendarTimeTrend >>> cal_trend_gen = CalendarTimeTrend("D", True, order=1) >>> cal_trend_gen.in_sample(index) Next, we normalize using the first time stamp >>> cal_trend_gen = CalendarTimeTrend("D", True, order=1, ... base_period=index[0]) >>> cal_trend_gen.in_sample(index) """ def __init__( self, freq: str, constant: bool = True, order: int = 0, *, base_period: Optional[Union[str, DateLike]] = None, ) -> None: super().__init__(freq) TimeTrendDeterministicTerm.__init__( self, constant=constant, order=order ) self._ref_i8 = 0 if base_period is not None: pr = pd.period_range(base_period, periods=1, freq=self._freq) self._ref_i8 = pr.asi8[0] self._base_period = None if base_period is None else str(base_period) @property def base_period(self) -> Optional[str]: """The base period""" return self._base_period
[docs] @classmethod def from_string( cls, freq: str, trend: str, base_period: Optional[Union[str, DateLike]] = None, ) -> "CalendarTimeTrend": """ Create a TimeTrend from a string description. Provided for compatibility with common string names. Parameters ---------- freq : str A string convertible to a pandas frequency. trend : {"n", "c", "t", "ct", "ctt"} The string representation of the time trend. The terms are: * "n": No trend terms * "c": A constant only * "t": Linear time trend only * "ct": A constant and a time trend * "ctt": A constant, a time trend and a quadratic time trend base_period : {str, pd.Timestamp}, default None The base period to use when computing the time stamps. This value is treated as 1 and so all other time indices are defined as the number of periods since or before this time stamp. If not provided, defaults to pandas base period for a PeriodIndex. Returns ------- TimeTrend The TimeTrend instance. """ constant = trend.startswith("c") order = 0 if "tt" in trend: order = 2 elif "t" in trend: order = 1 return cls(freq, constant, order, base_period=base_period)
def _terms( self, index: Union[pd.DatetimeIndex, pd.PeriodIndex], ratio: np.ndarray ) -> pd.DataFrame: if isinstance(index, pd.DatetimeIndex): index = index.to_period(self._freq) index_i8 = index.asi8 index_i8 = index_i8 - self._ref_i8 + 1 time = index_i8.astype(np.double) + ratio time = time[:, None] terms = self._get_terms(time) return pd.DataFrame(terms, columns=self._columns, index=index)
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample( self, index: Union[Sequence[Hashable], pd.Index] ) -> pd.DataFrame: index = self._index_like(index) index = self._check_index_type(index) ratio = self._compute_ratio(index) return self._terms(index, ratio)
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) def out_of_sample( self, steps: int, index: Union[Sequence[Hashable], pd.Index], forecast_index: Optional[Sequence[Hashable]] = None, ) -> pd.DataFrame: index = self._index_like(index) fcast_index = self._extend_index(index, steps, forecast_index) self._check_index_type(fcast_index) assert isinstance(fcast_index, (pd.PeriodIndex, pd.DatetimeIndex)) ratio = self._compute_ratio(fcast_index) return self._terms(fcast_index, ratio)
@property def _eq_attr(self) -> Tuple[Hashable, ...]: attr: Tuple[Hashable, ...] = ( self._constant, self._order, self._freq.freqstr, ) if self._base_period is not None: attr += (self._base_period,) return attr def __str__(self) -> str: value = TimeTrendDeterministicTerm.__str__(self) value = "Calendar" + value[:-1] + f", freq={self._freq.freqstr})" if self._base_period is not None: value = value[:-1] + f"base_period={self._base_period})" return value
[docs]class DeterministicProcess: """ Container class for deterministic terms. Directly supports constants, time trends, and either seasonal dummies or fourier terms for a single cycle. Additional deterministic terms beyond the set that can be directly initialized through the constructor can be added. Parameters ---------- index : {Sequence[Hashable], pd.Index} The index of the process. Should usually be the "in-sample" index when used in forecasting applications. period : {float, int}, default None The period of the seasonal or fourier components. Must be an int for seasonal dummies. If not provided, freq is read from index if available. constant : bool, default False Whether to include a constant. order : int, default 0 The order of the tim trend to include. For example, 2 will include both linear and quadratic terms. 0 exclude time trend terms. seasonal : bool = False Whether to include seasonal dummies fourier : int = 0 The order of the fourier terms to included. additional_terms : Sequence[DeterministicTerm] A sequence of additional deterministic terms to include in the process. drop : bool, default False A flag indicating to check for perfect collinearity and to drop any linearly dependent terms. See Also -------- TimeTrend Seasonality Fourier CalendarTimeTrend CalendarSeasonality CalendarFourier Examples -------- >>> from statsmodels.tsa.deterministic import DeterministicProcess >>> from pandas import date_range >>> index = date_range("2000-1-1", freq="M", periods=240) First a determinstic process with a constant and quadratic time trend. >>> dp = DeterministicProcess(index, constant=True, order=2) >>> dp.in_sample().head(3) const trend trend_squared 2000-01-31 1.0 1.0 1.0 2000-02-29 1.0 2.0 4.0 2000-03-31 1.0 3.0 9.0 Seasonal dummies are included by setting seasonal to True. >>> dp = DeterministicProcess(index, constant=True, seasonal=True) >>> dp.in_sample().iloc[:3,:5] const s(2,12) s(3,12) s(4,12) s(5,12) 2000-01-31 1.0 0.0 0.0 0.0 0.0 2000-02-29 1.0 1.0 0.0 0.0 0.0 2000-03-31 1.0 0.0 1.0 0.0 0.0 Fourier components can be used to alternatively capture seasonal patterns, >>> dp = DeterministicProcess(index, constant=True, fourier=2) >>> dp.in_sample().head(3) const sin(1,12) cos(1,12) sin(2,12) cos(2,12) 2000-01-31 1.0 0.000000 1.000000 0.000000 1.0 2000-02-29 1.0 0.500000 0.866025 0.866025 0.5 2000-03-31 1.0 0.866025 0.500000 0.866025 -0.5 Multiple Seasonalities can be captured using additional terms. >>> from statsmodels.tsa.deterministic import Fourier >>> index = date_range("2000-1-1", freq="D", periods=5000) >>> fourier = Fourier(period=365.25, order=1) >>> dp = DeterministicProcess(index, period=3, constant=True, ... seasonal=True, additional_terms=[fourier]) >>> dp.in_sample().head(3) const s(2,3) s(3,3) sin(1,365.25) cos(1,365.25) 2000-01-01 1.0 0.0 0.0 0.000000 1.000000 2000-01-02 1.0 1.0 0.0 0.017202 0.999852 2000-01-03 1.0 0.0 1.0 0.034398 0.999408 """ 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 @property def index(self) -> pd.Index: """The index of the process""" return self._index @property def terms(self) -> List[DeterministicTerm]: """The deterministic terms included in the process""" return self._deterministic_terms def _adjust_dummies(self, terms: List[pd.DataFrame]) -> List[pd.DataFrame]: has_const: Optional[bool] = None for dterm in self._deterministic_terms: if isinstance(dterm, (TimeTrend, CalendarTimeTrend)): has_const = has_const or dterm.constant if has_const is None: has_const = False for term in terms: const_col = (term == term.iloc[0]).all() & (term.iloc[0] != 0) has_const = has_const or const_col.any() drop_first = has_const for i, dterm in enumerate(self._deterministic_terms): is_dummy = dterm.is_dummy if is_dummy and drop_first: # drop first terms[i] = terms[i].iloc[:, 1:] drop_first = drop_first or is_dummy return terms def _remove_zeros_ones(self, terms: pd.DataFrame) -> pd.DataFrame: all_zero = np.all(terms == 0, axis=0) if np.any(all_zero): terms = terms.loc[:, ~all_zero] is_constant = terms.max(axis=0) == terms.min(axis=0) if np.sum(is_constant) > 1: # Retain first const_locs = np.where(is_constant)[0] is_constant[const_locs[:1]] = False terms = terms.loc[:, ~is_constant] return terms
[docs] @Appender(DeterministicTerm.in_sample.__doc__) def in_sample(self) -> pd.DataFrame: if self._cached_in_sample is not None: return self._cached_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.in_sample(index)) raw_terms = self._adjust_dummies(raw_terms) terms: pd.DataFrame = pd.concat(raw_terms, axis=1) terms = self._remove_zeros_ones(terms) if self._drop: terms_arr = to_numpy(terms) res = qr(terms_arr, mode="r", pivoting=True) r = res[0] p = res[-1] abs_diag = np.abs(np.diag(r)) tol = abs_diag[0] * terms_arr.shape[1] * np.finfo(float).eps rank = int(np.sum(abs_diag > tol)) rpx = r.T @ terms_arr keep = [0] last_rank = 1 # Find the left-most columns that produce full rank for i in range(1, terms_arr.shape[1]): curr_rank = np.linalg.matrix_rank(rpx[: i + 1, : i + 1]) if curr_rank > last_rank: keep.append(i) last_rank = curr_rank if curr_rank == rank: break if len(keep) == rank: terms = terms.iloc[:, keep] else: terms = terms.iloc[:, np.sort(p[:rank])] self._retain_cols = terms.columns self._cached_in_sample = terms return terms
[docs] @Appender(DeterministicTerm.out_of_sample.__doc__) 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 _extend_time_index( self, stop: pd.Timestamp, ) -> Union[pd.DatetimeIndex, pd.PeriodIndex]: index = self._index if isinstance(index, pd.PeriodIndex): return pd.period_range(index[0], end=stop, freq=index.freq) return pd.date_range(start=index[0], end=stop, freq=self._index_freq) def _range_from_range_index(self, start: int, stop: int) -> pd.DataFrame: index = self._index is_int64_index = is_int_index(index) assert isinstance(index, pd.RangeIndex) or is_int64_index if start < index[0]: raise ValueError(START_BEFORE_INDEX_ERR) if is_int64_index: idx_step = 1 else: try: idx_step = index.step except AttributeError: # TODO: Remove after pandas min ver is 1.0.0+ idx_step = index[-1] - index[-2] if len(index) > 1 else 1 if idx_step != 1 and ((start - index[0]) % idx_step) != 0: raise ValueError( f"The step of the index is not 1 (actual step={idx_step})." " start must be in the sequence that would have been " "generated by the index." ) if is_int64_index: new_idx = NumericIndex(np.arange(start, stop)) else: new_idx = pd.RangeIndex(start, stop, step=idx_step) if new_idx[-1] <= self._index[-1]: # In-sample only in_sample = self.in_sample() in_sample = in_sample.loc[new_idx] return in_sample elif new_idx[0] > self._index[-1]: # Out of-sample only next_value = index[-1] + idx_step if new_idx[0] != next_value: tmp = pd.RangeIndex(next_value, stop, step=idx_step) oos = self.out_of_sample(tmp.shape[0], forecast_index=tmp) return oos.loc[new_idx] return self.out_of_sample(new_idx.shape[0], forecast_index=new_idx) # Using some from each in and out of sample in_sample_loc = new_idx <= self._index[-1] in_sample_idx = new_idx[in_sample_loc] out_of_sample_idx = new_idx[~in_sample_loc] in_sample_exog = self.in_sample().loc[in_sample_idx] oos_exog = self.out_of_sample( steps=out_of_sample_idx.shape[0], forecast_index=out_of_sample_idx ) return pd.concat([in_sample_exog, oos_exog], axis=0) def _range_from_time_index( self, start: pd.Timestamp, stop: pd.Timestamp ) -> pd.DataFrame: index = self._index if isinstance(self._index, pd.PeriodIndex): if isinstance(start, pd.Timestamp): start = start.to_period(freq=self._index_freq) if isinstance(stop, pd.Timestamp): stop = stop.to_period(freq=self._index_freq) if start < index[0]: raise ValueError(START_BEFORE_INDEX_ERR) if stop <= self._index[-1]: return self.in_sample().loc[start:stop] new_idx = self._extend_time_index(stop) oos_idx = new_idx[new_idx > index[-1]] oos = self.out_of_sample(oos_idx.shape[0], oos_idx) if start >= oos_idx[0]: return oos.loc[start:stop] both = pd.concat([self.in_sample(), oos], axis=0) return both.loc[start:stop] def _int_to_timestamp(self, value: int, name: str) -> pd.Timestamp: if value < 0: raise ValueError(f"{name} must be non-negative.") if value < self._index.shape[0]: return self._index[value] add_periods = value - (self._index.shape[0] - 1) + 1 index = self._index if isinstance(self._index, pd.PeriodIndex): pr = pd.period_range( index[-1], freq=self._index_freq, periods=add_periods ) return pr[-1].to_timestamp() dr = pd.date_range( index[-1], freq=self._index_freq, periods=add_periods ) return dr[-1]
[docs] def range( self, start: Union[IntLike, DateLike, str], stop: Union[IntLike, DateLike, str], ) -> pd.DataFrame: """ Deterministic terms spanning a range of observations Parameters ---------- start : {int, str, dt.datetime, pd.Timestamp, np.datetime64} The first observation. stop : {int, str, dt.datetime, pd.Timestamp, np.datetime64} The final observation. Inclusive to match most prediction function in statsmodels. Returns ------- DataFrame A data frame of deterministic terms """ if not self._extendable: raise TypeError( """The index in the deterministic process does not \ support extension. Only PeriodIndex, DatetimeIndex with a frequency, \ RangeIndex, and NumericIndex 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,) or is_int_index(self._index): 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 _validate_index(self) -> None: if isinstance(self._index, pd.PeriodIndex): self._index_freq = self._index.freq self._extendable = True elif isinstance(self._index, pd.DatetimeIndex): self._index_freq = self._index.freq or self._index.inferred_freq self._extendable = self._index_freq is not None elif isinstance(self._index, pd.RangeIndex): self._extendable = True elif is_int_index(self._index): self._extendable = self._index[0] == 0 and np.all( np.diff(self._index) == 1 )