[ENH] Introduce AutoSARIMA forecaster

aeon/forecasting/__init__.py

@@ -5,9 +5,17 @@
     "BaseForecaster",
     "RegressionForecaster",
     "ETSForecaster",
+    "ARIMAForecaster",
+    "SARIMAForecaster",
+    "AutoARIMAForecaster",
+    "AutoSARIMAForecaster",
 ]
 
+from aeon.forecasting._arima import ARIMAForecaster
+from aeon.forecasting._auto_arima import AutoARIMAForecaster
+from aeon.forecasting._auto_sarima import AutoSARIMAForecaster
 from aeon.forecasting._ets import ETSForecaster
 from aeon.forecasting._naive import NaiveForecaster
 from aeon.forecasting._regression import RegressionForecaster
+from aeon.forecasting._sarima import SARIMAForecaster
 from aeon.forecasting.base import BaseForecaster

aeon/forecasting/_arima.py

@@ -0,0 +1,273 @@
+"""ARIMAForecaster.
+
+An implementation of the ARIMA forecasting algorithm.
+"""
+
+__maintainer__ = ["alexbanwell1", "TonyBagnall"]
+__all__ = ["ARIMAForecaster"]
+
+import numpy as np
+from numba import njit
+
+from aeon.forecasting.base import BaseForecaster
+from aeon.utils.optimisation._nelder_mead import nelder_mead
+
+
+class ARIMAForecaster(BaseForecaster):
+    """AutoRegressive Integrated Moving Average (ARIMA) forecaster.
+
+    The model automatically selects the parameters of the model based
+    on information criteria, such as AIC.
+
+    Parameters
+    ----------
+    p : int, default=1,
+        Autoregressive (p) order of the ARIMA model
+    d : int, default=0,
+        Differencing (d) order of the ARIMA model
+    q : int, default=1,
+        Moving average (q) order of the ARIMA model
+    constant_term: bool = False,
+        Presence of a constant/intercept term in the model.
+    horizon : int, default=1
+        The forecasting horizon, i.e., the number of steps ahead to predict.
+
+    Attributes
+    ----------
+    data_ : np.ndarray
+        Original training series values.
+    differenced_data_ : np.ndarray
+        Differenced version of the training data used for stationarity.
+    residuals_ : np.ndarray
+        Residual errors from the fitted model.
+    aic_ : float
+        Akaike Information Criterion for the selected model.
+    p, d, q : int
+        Parameters passed to the forecaster see p_, d_, q_.
+    p_, d_, q_ : int
+        Orders of the ARIMA model: autoregressive (p), differencing (d),
+        and moving average (q) terms.
+    constant_term : bool
+        Parameters passed to the forecaster see constant_term_.
+    constant_term_ : bool
+        Whether to include a constant/intercept term in the model.
+    c_ : float
+        Estimated constant term (internal use).
+    phi_ : np.ndarray
+        Coefficients for the non-seasonal autoregressive terms.
+    theta_ : np.ndarray
+        Coefficients for the non-seasonal moving average terms.
+
+    References
+    ----------
+    .. [1] R. J. Hyndman and G. Athanasopoulos,
+       Forecasting: Principles and Practice. OTexts, 2014.
+       https://otexts.com/fpp3/
+
+    Examples
+    --------
+    >>> from aeon.forecasting import ARIMAForecaster
+    >>> from aeon.datasets import load_airline
+    >>> y = load_airline()
+    >>> forecaster = ARIMAForecaster(p=2,d=1)
+    >>> forecaster.fit(y)
+    ARIMAForecaster(d=1, p=2)
+    >>> forecaster.predict()
+    474.49449...
+    """
+
+    def __init__(
+        self,
+        p: int = 1,
+        d: int = 0,
+        q: int = 1,
+        constant_term: bool = False,
+    ):
+        super().__init__(horizon=1, axis=1)
+        self.data_ = []
+        self.differenced_data_ = []
+        self.residuals_ = []
+        self.fitted_values_ = []
+        self.aic_ = 0
+        self.p = p
+        self.d = d
+        self.q = q
+        self.constant_term = constant_term
+        self.p_ = 0
+        self.d_ = 0
+        self.q_ = 0
+        self.constant_term_ = False
+        self.model_ = []
+        self.c_ = 0
+        self.phi_ = 0
+        self.theta_ = 0
+        self.parameters_ = []
+
+    def _fit(self, y, exog=None):
+        """Fit AutoARIMA forecaster to series y.
+
+        Fit a forecaster to predict self.horizon steps ahead using y.
+
+        Parameters
+        ----------
+        y : np.ndarray
+            A time series on which to learn a forecaster to predict horizon ahead
+        exog : np.ndarray, default =None
+            Optional exogenous time series data assumed to be aligned with y
+
+        Returns
+        -------
+        self
+            Fitted ARIMAForecaster.
+        """
+        self.p_ = self.p
+        self.d_ = self.d
+        self.q_ = self.q
+        self.constant_term_ = self.constant_term
+        self.data_ = np.array(y.squeeze(), dtype=np.float64)
+        self.model_ = np.array(
+            (1 if self.constant_term else 0, self.p, self.q), dtype=np.int32
+        )
+        self.differenced_data_ = np.diff(self.data_, n=self.d)
+        (self.parameters_, self.aic_) = nelder_mead(
+            _arima_model_wrapper,
+            np.sum(self.model_[:3]),
+            self.differenced_data_,
+            self.model_,
+        )
+        (self.c_, self.phi_, self.theta_) = _extract_params(
+            self.parameters_, self.model_
+        )
+        (self.aic_, self.residuals_, self.fitted_values_) = _arima_model(
+            self.parameters_,
+            _calc_arima,
+            self.differenced_data_,
+            self.model_,
+            np.empty(0),
+        )
+        return self
+
+    def _predict(self, y=None, exog=None):
+        """
+        Predict the next horizon steps ahead.
+
+        Parameters
+        ----------
+        y : np.ndarray, default = None
+            A time series to predict the next horizon value for. If None,
+            predict the next horizon value after series seen in fit.
+        exog : np.ndarray, default =None
+            Optional exogenous time series data assumed to be aligned with y
+
+        Returns
+        -------
+        array[float]
+            Predictions len(y) steps ahead of the data seen in fit.
+        If y is None, then predict 1 step ahead of the data seen in fit.
+        """
+        if y is not None:
+            combined_data = np.concatenate((self.data_, y.flatten()))
+        else:
+            combined_data = self.data_
+        n = len(self.data_)
+        differenced_data = np.diff(combined_data, n=self.d_)
+        _aic, _residuals, predicted_values = _arima_model(
+            self.parameters_,
+            _calc_arima,
+            differenced_data,
+            self.model_,
+            self.residuals_,
+        )
+        init = combined_data[n - self.d_ : n]
+        x = np.concatenate((init, predicted_values))
+        for _ in range(self.d_):
+            x = np.cumsum(x)
+        return x[self.d_ :]
+
+
+@njit(cache=True, fastmath=True)
+def _aic(residuals, num_params):
+    """Calculate the log-likelihood of a model."""
+    variance = np.mean(residuals**2)
+    liklihood = len(residuals) * (np.log(2 * np.pi) + np.log(variance) + 1)
+    return liklihood + 2 * num_params
+
+
+@njit(fastmath=True)
+def _arima_model_wrapper(params, data, model):
+    return _arima_model(params, _calc_arima, data, model, np.empty(0))[0]
+
+
+# Define the ARIMA(p, d, q) likelihood function
+@njit(cache=True, fastmath=True)
+def _arima_model(params, base_function, data, model, residuals):
+    """Calculate the log-likelihood of an ARIMA model given the parameters."""
+    formatted_params = _extract_params(params, model)  # Extract parameters
+
+    # Initialize residuals
+    n = len(data)
+    m = len(residuals)
+    num_predictions = n - m + 1
+    residuals = np.concatenate((residuals, np.zeros(num_predictions - 1)))
+    expect_full_history = m > 0  # I.e. we've been provided with some residuals
+    fitted_values = np.zeros(num_predictions)
+    for t in range(num_predictions):
+        fitted_values[t] = base_function(
+            data,
+            model,
+            m + t,
+            formatted_params,
+            residuals,
+            expect_full_history,
+        )
+        if t != num_predictions - 1:
+            # Only calculate residuals for the predictions we have data for
+            residuals[m + t] = data[m + t] - fitted_values[t]
+    return _aic(residuals, len(params)), residuals, fitted_values
+
+
+@njit(cache=True, fastmath=True)
+def _extract_params(params, model):
+    """Extract ARIMA parameters from the parameter vector."""
+    if len(params) != np.sum(model):
+        previous_length = np.sum(model)
+        model = model[:-1]  # Remove the seasonal period
+        if len(params) != np.sum(model):
+            raise ValueError(
+                f"Expected {previous_length} parameters for a non-seasonal model or \
+                    {np.sum(model)} parameters for a seasonal model, got {len(params)}"
+            )
+    starts = np.cumsum(np.concatenate((np.zeros(1, dtype=np.int32), model[:-1])))
+    n = len(starts)
+    max_len = np.max(model)
+    result = np.full((n, max_len), np.nan, dtype=params.dtype)
+    for i in range(n):
+        length = model[i]
+        start = starts[i]
+        result[i, :length] = params[start : start + length]
+    return result
+
+
+@njit(cache=True, fastmath=True)
+def _calc_arima(data, model, t, formatted_params, residuals, expect_full_history=False):
+    """Calculate the ARIMA forecast for time t."""
+    if len(model) != 3:
+        raise ValueError("Model must be of the form (c, p, q)")
+    p = model[1]
+    q = model[2]
+    if expect_full_history and (t - p < 0 or t - q < 0):
+        raise ValueError(
+            f"Insufficient data for ARIMA model at time {t}. "
+            f"Expected at least {p} past values for AR and {q} for MA."
+        )
+    # AR part
+    phi = formatted_params[1][:p]
+    ar_term = 0 if (t - p) < 0 else np.dot(phi, data[t - p : t][::-1])
+
+    # MA part
+    theta = formatted_params[2][:q]
+    ma_term = 0 if (t - q) < 0 else np.dot(theta, residuals[t - q : t][::-1])
+
+    c = formatted_params[0][0] if model[0] else 0
+    y_hat = c + ar_term + ma_term
+    return y_hat