Merge a007dd7 into 428b49c

statsmodels/tsa/automatic/__init__.py

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+from .forecast import Forecast
+from .forecast import ForecastSet

statsmodels/tsa/automatic/exponentialsmoothing.py

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+"""Automatic selection of the Exponential Smoothing Model."""
+import numpy as np
+import warnings
+import statsmodels.api as sm
+
+
+def auto_es(endog, measure='aic', seasonal_periods=1, damped=False,
+            additive_only=False, alpha=None, beta=None,
+            gamma=None, phi=None):
+    """Perform automatic calculation of the parameters used in ES models.
+
+    This uses a brute force approach to traverse through all the possible
+    parameters of the model and select the best model based on the measure values.
+
+    Parameters
+    ----------
+    endog : list
+        input contains the time series data over a period of time.
+    measure : str
+        specifies which information measure to use for model evaluation.
+        'aic' is the default measure.
+    seasonal_periods : int
+        the length of a seaonal period.
+    damped : boolean
+        includes damped trends.
+    additive_only : boolean
+        Allows only additive trend and seasonal models.
+    alpha : float
+        Smoothing level.
+    beta : float
+        Smoothing slope.
+    gamma : float
+        Smoothing seasonal.
+    phi : float
+        damping slope.
+
+    Returns
+    -------
+    model : pair
+        Pair containing trend,seasonal component type.
+    Notes
+    -----
+    Status : Work In Progress.
+
+    """
+    if damped:
+        trends = ["add", "mul"]
+    else:
+        trends = ["add", "mul", None]
+    # damped
+    seasonal = ["add", "mul", None]
+    if additive_only:
+        trends = ["add"]
+        seasonal = ["add"]
+    min_measure = np.inf
+    model = [None, None]
+    for t in trends:
+        if seasonal_periods > 1:
+            for s in seasonal:
+                try:
+                    mod = sm.tsa.ExponentialSmoothing(endog, trend=t,
+                                                      seasonal=s,
+                                                      seasonal_periods=seasonal_periods)
+                    res = mod.fit(smoothing_level=alpha, smoothing_slope=beta,
+                                  smoothing_seasonal=gamma, damping_slope=phi)
+                    if getattr(res, measure) < min_measure:
+                        min_measure = getattr(res, measure)
+                        model = [t, s]
+                except Exception as e:
+                    warnings.warn(str(e))
+        else:
+            try:
+                mod = sm.tsa.ExponentialSmoothing(endog, trend=t,
+                                                  seasonal_periods=seasonal_periods)
+                res = mod.fit(smoothing_level=alpha, smoothing_slope=beta,
+                              smoothing_seasonal=gamma, damping_slope=phi)
+                if getattr(res, measure) < min_measure:
+                    min_measure = getattr(res, measure)
+                    model = [t, None]
+            except Exception as e:
+                warnings.warn(str(e))
+    return model

statsmodels/tsa/automatic/forecast.py

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+"""Classes to hold the Forecast results individually and in sets."""
+import numpy as np
+import warnings
+import statsmodels.api as sm
+from statsmodels.tools.decorators import cache_readonly
+
+from statsmodels.tsa.automatic import sarimax
+from statsmodels.tsa.automatic import exponentialsmoothing
+
+
+class Forecast(object):
+    """Class to hold the data of a single forecast model."""
+
+    def __init__(self, endog, model, s=1, test_sample=0.2, auto_params=False,
+                 **spec):
+        """Intialize the data for the Forecast class.
+
+        Parameters
+        ----------
+        endog : list
+            input contains the time series data over a period of time.
+        model : class
+            specifies which model to use for forecasting the data.
+        s : int
+            the length of a seaonal period.
+        test_sample : float
+            ratio of the enod to keep for testing.
+        auto_params : boolean
+            ensures automatic selection of model parameters.
+
+        Notes
+        -----
+        Status : Work In Progress.
+
+        """
+        # TODO: make date selection of test sample more robust
+        if test_sample is None:
+            self.endog_training = endog
+            self.indicator = True
+        else:
+            if type(test_sample) == str:
+                self.endog_training = endog[:test_sample][:-1]
+                self.endog_test = endog[test_sample:]
+            else:
+                if type(test_sample) == float or type(test_sample) == int:
+                    if test_sample > 0.0 and test_sample < 1.0:
+                        # here test_sample is containing the number of
+                        # observations to consider for the endog_test
+                        test_sample = int(test_sample * len(endog))
+                self.endog_training = endog[:-test_sample]
+                self.endog_test = endog[-test_sample:]
+        if auto_params:
+            if (model == sm.tsa.SARIMAX):
+                if s > 1:
+                    trend, p, d, q, P, D, Q = sarimax.auto_order(self.endog_training,
+                                                                 stepwise=True,
+                                                                 s=s, **spec)
+                    # update dictionary
+                    spec['order'] = (p, d, q)
+                    spec['seasonal_order'] = (P, D, Q, s)
+                    if trend:
+                        spec['trend'] = 'c'
+                else:
+                    trend, p, d, q = sarimax.auto_order(self.endog_training,
+                                                        stepwise=True, **spec)
+                    # update dictionary
+                    spec['order'] = (p, d, q)
+                    if trend:
+                        spec['trend'] = 'c'
+            if(model == sm.tsa.ExponentialSmoothing):
+                mod = exponentialsmoothing.auto_es(self.endog_training, **spec)
+                # update dictionary
+                spec['trend'] = mod[0]
+                spec['seasonal'] = mod[1]
+        # Fitting the appropriate model
+        self.model = model(self.endog_training, **spec)
+        # self.model = model(endog, **spec)
+        self.results = self.model.fit()
+
+    @property
+    def resid(self):
+        """(array) The list of residuals while fitting the model."""
+        return self.results.resid
+
+    @property
+    def fittedvalues(self):
+        """(array) The list of fitted values of the time-series model."""
+        return self.results.fittedvalues
+
+    @cache_readonly
+    def nobs_training(self):
+        """(int) Number of observations in the training set."""
+        return len(self.endog_training)
+
+    @cache_readonly
+    def nobs_test(self):
+        """(int) Number of observations in the test set."""
+        return len(self.endog_test)
+
+    @cache_readonly
+    def forecasts(self):
+        """(array) The model forecast values."""
+        return self.results.forecast(self.nobs_test)
+
+    # In this case we'll be computing accuracy using forecast errors
+    # instead of residual values. The forecast error is calculated on test set.
+    @cache_readonly
+    def forecasts_error(self):
+        """(array) The model forecast errors."""
+        return self.endog_test - self.forecasts
+
+    @cache_readonly
+    def mae(self):
+        """(float) Mean Absolute Error."""
+        return np.mean(np.abs(self.forecasts_error))
+
+    @cache_readonly
+    def rmse(self):
+        """(float) Root mean squared error."""
+        return np.sqrt(np.mean(self.forecasts_error ** 2))
+
+    @cache_readonly
+    def mape(self):
+        """(float) Mean absolute percentage error."""
+        return np.mean(np.abs((self.forecasts_error) / self.endog_test)) * 100
+
+    @cache_readonly
+    def smape(self):
+        """(float) symmetric Mean absolute percentage error."""
+        return np.mean(
+                200*np.abs(self.forecasts_error) /
+                np.abs(self.endog_test + self.forecasts))
+
+    @cache_readonly
+    def mase(self):
+        """(float) Mean Absolute Scaled Error."""
+        # for non-seasonal time series
+        e_j = self.forecasts_error
+        sum_v = 0
+        for val in range(2, self.nobs_training):
+            sum_v += (self.endog_training[val] - self.endog_training[val - 1])
+        q_j = e_j*(self.nobs_training-1)/sum_v
+        return np.mean(np.abs(q_j))
+
+
+class ForecastSet(object):
+    """Class to hold various Forecast objects.
+
+    The class holds various Forecast objects and selects
+    the best Forecast object based on some evaluation measure.
+    """
+
+    def __init__(self, endog, test_sample):
+        """Initialize the values of the ForecastSet class.
+
+        Parameters
+        ----------
+        endog : list
+            input contains the time series data over a period of time.
+        test_sample : float
+            ratio of the enod to keep for testing.
+
+        """
+        self.endog = endog
+        self.test_sample = test_sample
+        self.models = []
+
+    def add(self, model, **spec):
+        """Add a Forecast object to this ForecastSet.
+
+        Parameters
+        ----------
+        model : class
+            specifies which model to use for forecasting the data.
+
+        """
+        fcast = Forecast(self.endog, model, self.test_sample, **spec)
+        self.models.append(fcast)
+
+    def add_fcast(self, fcast_ob):
+        """Add a Forecast object to this ForecastSet.
+
+        Parameters
+        ----------
+        fcast_ob : Forecast object
+            already created Forecast object class.
+
+        """
+        self.models.append(fcast_ob)
+
+    def validate(self):
+        train = self.models[0].endog_training
+        test = self.models[0].endog_test
+        for i in range(1, len(self.models)):
+            if not ((self.models[i].endog_training.equals(train)) and
+                    (self.models[i].endog_test.equals(test))):
+                return False
+        return True
+
+
+    def select(self, measure='mae'):
+        """Select the best forecast based on criteria provided by the user.
+
+        Parameters
+        ----------
+        measure : str
+            specifies the measure to select the best performing model.
+            mae is the default measure.
+        Return
+        ----------
+        model : class
+            specifies which model to use for forecasting the data.
+
+        """
+        if (self.validate()):
+            measure_vals = np.zeros(len(self.models))
+            for mod in range(len(self.models)):
+                measure_vals[mod] = getattr(self.models[mod], measure)
+            min_measure = measure_vals.min()
+            model = np.where(measure_vals == min_measure)
+            # print(self.models[0][0])
+            return self.models[model[0][0]]
+        else:
+            warnings.warn("Cannot Compare models with diffetent training and test data")