stationarity.py 文件源码

def is_stationary(self, x):
        """Test whether the time series is stationary.

        Parameters
        ----------
        x : array-like, shape=(n_samples,)
            The time series vector.
        """
        if not self._base_case(x):
            return np.nan, False

        # ensure vector
        x = column_or_1d(check_array(
            x, ensure_2d=False, dtype=DTYPE,
            force_all_finite=True))  # type: np.ndarray
        n = x.shape[0]

        # check on status of null
        null = self.null

        # fit a model on an arange to determine the residuals
        if null == 'trend':
            t = np.arange(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.216, 0.176, 0.146, 0.119)
        elif null == 'level':
            t = np.ones(n).reshape(n, 1)

            # these numbers came out of the R code.. I've found 0 doc for these
            table = c(0.739, 0.574, 0.463, 0.347)
        else:
            raise ValueError("null must be one of %r" % self._valid)

        # fit the model
        lm = LinearRegression().fit(t, x)
        e = x - lm.predict(t)  # residuals
        tablep = c(0.01, 0.025, 0.05, 0.10)

        s = np.cumsum(e)
        eta = (s * s).sum() / (n**2)
        s2 = (e * e).sum() / n

        scalar, denom = 10, 14
        if self.lshort:
            scalar, denom = 3, 13
        l = int(np.trunc(scalar * np.sqrt(n) / denom))

        # compute the C subroutine
        s2 = C_tseries_pp_sum(e, n, l, s2)
        stat = eta / s2

        # do approximation
        _, pval = approx(table, tablep, xout=stat, rule=2)

        # R does a test for rule=1, but we don't want to do that, because they
        # just do it to issue a warning in case the P-value is smaller/greater
        # than the printed value is.
        return pval[0], pval[0] < self.alpha