"""Autoregressive Integrated Moving Average Model. The three parameters (p, d, q) are the AR order, the degree of differencing, and the MA order. More information here: https://www.statsmodels.org/devel/generated/statsmodels.tsa.arima.model.ARIMA.html."""
from typing import Dict, Hashable, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
from pandas.api.types import is_integer_dtype
from skopt.space import Integer
from sktime.forecasting.base import ForecastingHorizon
from evalml.model_family import ModelFamily
from evalml.pipelines.components.estimators import Estimator
from evalml.pipelines.components.utils import convert_bool_to_double, match_indices
from evalml.problem_types import ProblemTypes
from evalml.utils import (
import_or_raise,
infer_feature_types,
)
[docs]class ARIMARegressor(Estimator):
"""Autoregressive Integrated Moving Average Model. The three parameters (p, d, q) are the AR order, the degree of differencing, and the MA order. More information here: https://www.statsmodels.org/devel/generated/statsmodels.tsa.arima.model.ARIMA.html.
Currently ARIMARegressor isn't supported via conda install. It's recommended that it be installed via PyPI.
Args:
time_index (str): Specifies the name of the column in X that provides the datetime objects. Defaults to None.
trend (str): Controls the deterministic trend. Options are ['n', 'c', 't', 'ct'] where 'c' is a constant term,
't' indicates a linear trend, and 'ct' is both. Can also be an iterable when defining a polynomial, such
as [1, 1, 0, 1].
start_p (int): Minimum Autoregressive order. Defaults to 2.
d (int): Minimum Differencing degree. Defaults to 0.
start_q (int): Minimum Moving Average order. Defaults to 2.
max_p (int): Maximum Autoregressive order. Defaults to 5.
max_d (int): Maximum Differencing degree. Defaults to 2.
max_q (int): Maximum Moving Average order. Defaults to 5.
seasonal (boolean): Whether to fit a seasonal model to ARIMA. Defaults to True.
sp (int or str): Period for seasonal differencing, specifically the number of periods in each season. If "detect", this
model will automatically detect this parameter (given the time series is a standard frequency) and will fall
back to 1 (no seasonality) if it cannot be detected. Defaults to 1.
n_jobs (int or None): Non-negative integer describing level of parallelism used for pipelines. Defaults to -1.
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
name = "ARIMA Regressor"
hyperparameter_ranges = {
"start_p": Integer(1, 3),
"d": Integer(0, 2),
"start_q": Integer(1, 3),
"max_p": Integer(3, 10),
"max_d": Integer(2, 5),
"max_q": Integer(3, 10),
"seasonal": [True, False],
}
"""{
"start_p": Integer(1, 3),
"d": Integer(0, 2),
"start_q": Integer(1, 3),
"max_p": Integer(3, 10),
"max_d": Integer(2, 5),
"max_q": Integer(3, 10),
"seasonal": [True, False],
}"""
model_family = ModelFamily.ARIMA
"""ModelFamily.ARIMA"""
supported_problem_types = [ProblemTypes.TIME_SERIES_REGRESSION]
"""[ProblemTypes.TIME_SERIES_REGRESSION]"""
max_rows = 1000
max_cols = 7
def __init__(
self,
time_index: Optional[Hashable] = None,
trend: Optional[str] = None,
start_p: int = 2,
d: int = 0,
start_q: int = 2,
max_p: int = 5,
max_d: int = 2,
max_q: int = 5,
seasonal: bool = True,
sp: int = 1,
n_jobs: int = -1,
random_seed: Union[int, float] = 0,
maxiter: int = 10,
use_covariates: bool = True,
**kwargs,
):
self.preds_95_upper = None
self.preds_95_lower = None
parameters = {
"trend": trend,
"start_p": start_p,
"d": d,
"start_q": start_q,
"max_p": max_p,
"max_d": max_d,
"max_q": max_q,
"seasonal": seasonal,
"maxiter": maxiter,
"n_jobs": n_jobs,
}
parameters.update(kwargs)
arima_model_msg = (
"sktime is not installed. Please install using `pip install sktime.`"
)
sktime_arima = import_or_raise(
"sktime.forecasting.arima",
error_msg=arima_model_msg,
)
arima_model = sktime_arima.AutoARIMA(**parameters)
parameters["use_covariates"] = use_covariates
parameters["time_index"] = time_index
self.sp = sp
self.use_covariates = use_covariates
super().__init__(
parameters=parameters,
component_obj=arima_model,
random_seed=random_seed,
)
def _remove_datetime(
self,
data: pd.DataFrame,
features: bool = False,
) -> pd.DataFrame:
if data is None:
return None
data_no_dt = data.ww.copy()
if isinstance(
data_no_dt.index,
(pd.DatetimeIndex, pd.PeriodIndex, pd.IntervalIndex),
):
data_no_dt = data_no_dt.ww.reset_index(drop=True)
if features:
data_no_dt = data_no_dt.ww.select(exclude=["Datetime"])
return data_no_dt
def _set_forecast(self, X: pd.DataFrame):
# we can only calculate the difference if the indices are of the same type
units_diff = 1
if isinstance(X.index[0], type(self.last_X_index)) and isinstance(
X.index,
pd.DatetimeIndex,
):
dates_diff = pd.date_range(
start=self.last_X_index,
end=X.index[0],
freq=X.index.freq,
)
units_diff = len(dates_diff) - 1
elif is_integer_dtype(type(X.index[0])) and is_integer_dtype(
type(self.last_X_index),
):
units_diff = X.index[0] - self.last_X_index
fh_ = ForecastingHorizon(
[units_diff + i for i in range(len(X))],
is_relative=True,
)
return fh_
def _get_sp(self, X: pd.DataFrame) -> int:
if X is None:
return 1
freq_mappings = {
"D": 7,
"M": 12,
"Q": 4,
}
time_index = self._parameters.get("time_index", None)
sp = self.sp
if sp == "detect":
inferred_freqs = X.ww.infer_temporal_frequencies()
freq = inferred_freqs.get(time_index, None)
sp = 1
if freq is not None:
sp = freq_mappings.get(freq[:1], 1)
return sp
[docs] def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
"""Fits ARIMA regressor to data.
Args:
X (pd.DataFrame): The input training data of shape [n_samples, n_features].
y (pd.Series): The target training data of length [n_samples].
Returns:
self
Raises:
ValueError: If y was not passed in.
"""
X, y = self._manage_woodwork(X, y)
if X is not None:
X = X.ww.fillna(X.mean())
if y is None:
raise ValueError("ARIMA Regressor requires y as input.")
sp = self._get_sp(X)
self._component_obj.sp = sp
self.last_X_index = X.index[-1] if X is not None else y.index[-1]
X = self._remove_datetime(X, features=True)
if X is not None:
X = convert_bool_to_double(X)
y = self._remove_datetime(y)
X, y = match_indices(X, y)
if X is not None and not X.empty and self.use_covariates:
self._component_obj.fit(y=y, X=X)
else:
self._component_obj.fit(y=y)
return self
def _manage_types_and_forecast(self, X: pd.DataFrame) -> tuple:
fh_ = self._set_forecast(X)
X = X.ww.select(exclude=["Datetime"])
X = convert_bool_to_double(X)
return X, fh_
@staticmethod
def _parse_prediction_intervals(
y_pred_intervals: pd.DataFrame,
conf_int: float,
) -> Tuple[pd.Series, pd.Series]:
coverage_name = y_pred_intervals.columns[0][0]
preds_lower = y_pred_intervals.loc(axis=1)[(coverage_name, conf_int, "lower")]
preds_upper = y_pred_intervals.loc(axis=1)[(coverage_name, conf_int, "upper")]
preds_lower.name = None
preds_upper.name = None
return preds_lower, preds_upper
[docs] def predict(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> pd.Series:
"""Make predictions using fitted ARIMA regressor.
Args:
X (pd.DataFrame): Data of shape [n_samples, n_features].
y (pd.Series): Target data.
Returns:
pd.Series: Predicted values.
Raises:
ValueError: If X was passed to `fit` but not passed in `predict`.
"""
X, y = self._manage_woodwork(X, y)
X, fh_ = self._manage_types_and_forecast(X=X)
if not X.empty and self.use_covariates:
if fh_[0] != 1:
# pmdarima (which sktime uses under the hood) only forecasts off the training data
# but sktime circumvents this by predicting everything from the end of training data to the date / periods requested
# and only returning the values for dates / periods given to sktime. Because of this,
# pmdarima requires the number of covariate rows to equal the length of the total number of periods (X.shape[0] == fh_[-1]) if covariates are used.
# We circument this by adding arbitrary rows to the start of X since sktime discards these values when predicting.
num_rows_diff = fh_[-1] - X.shape[0]
filler = pd.DataFrame(
columns=X.columns,
index=range(num_rows_diff),
).fillna(0)
X_ = pd.concat([filler, X], ignore_index=True)
X_.ww.init(schema=X.ww.schema)
else:
X_ = X
y_pred_intervals = self._component_obj.predict_interval(
fh=fh_,
X=X_,
coverage=[0.95],
)
else:
y_pred_intervals = self._component_obj.predict_interval(
fh=fh_,
coverage=[0.95],
)
y_pred_intervals.index = X.index
(
self.preds_95_lower,
self.preds_95_upper,
) = ARIMARegressor._parse_prediction_intervals(y_pred_intervals, 0.95)
y_pred = pd.concat((self.preds_95_lower, self.preds_95_upper), axis=1).mean(
axis=1,
)
return infer_feature_types(y_pred)
[docs] def get_prediction_intervals(
self,
X: pd.DataFrame,
y: pd.Series = None,
coverage: List[float] = None,
predictions: pd.Series = None,
) -> Dict[str, pd.Series]:
"""Find the prediction intervals using the fitted ARIMARegressor.
Args:
X (pd.DataFrame): Data of shape [n_samples, n_features].
y (pd.Series): Target data. Optional.
coverage (list[float]): A list of floats between the values 0 and 1 that the upper and lower bounds of the
prediction interval should be calculated for.
predictions (pd.Series): Not used for ARIMA regressor.
Returns:
dict: Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.
"""
if coverage is None:
coverage = [0.95]
X, y = self._manage_woodwork(X, y)
X, fh_ = self._manage_types_and_forecast(X=X)
prediction_interval_result = {}
if not X.empty and self.use_covariates:
y_pred_intervals = self._component_obj.predict_interval(
fh=fh_,
X=X,
coverage=coverage,
)
else:
y_pred_intervals = self._component_obj.predict_interval(
fh=fh_,
coverage=coverage,
)
y_pred_intervals.index = X.index
for conf_int in coverage:
if (
conf_int == 0.95
and self.preds_95_lower is not None
and self.preds_95_upper is not None
):
prediction_interval_result[f"{conf_int}_lower"] = self.preds_95_lower
prediction_interval_result[f"{conf_int}_upper"] = self.preds_95_upper
continue
preds_lower, preds_upper = ARIMARegressor._parse_prediction_intervals(
y_pred_intervals,
conf_int,
)
prediction_interval_result[f"{conf_int}_lower"] = preds_lower
prediction_interval_result[f"{conf_int}_upper"] = preds_upper
return prediction_interval_result
@property
def feature_importance(self) -> np.ndarray:
"""Returns array of 0's with a length of 1 as feature_importance is not defined for ARIMA regressor."""
return np.zeros(1)
