示例#1
0
    def preprocess(self, ts, train_percentage):
        if len(ts.shape) == 1:
            ts = ts.reshape(1, len(ts))

        # Regress original ts
        X_orig, y_orig = sliding_window(ts, p=self.q, drop_last_dim=True)
        self.regressor_model.preprocess(ts, 1.0)
        self.regressor_model.fit(X_orig, y_orig)

        # Predict with regressor model and substract original to obtain residuals.
        pred = self.regressor_model.model.predict(X_orig)
        pred = pred.reshape(1, len(pred))
        residual_ts = np.concatenate((np.zeros(
            (1, self.q)), pred), axis=1) - ts

        # Define sliding window on residuals and compute historic vols from it
        X_residual, y_residual = sliding_window(residual_ts,
                                                p=self.q,
                                                drop_last_dim=False)
        y_sigmas = X_residual.std(axis=1)
        y_sigmas_squared = y_sigmas * y_sigmas
        X_residual_squared = X_residual * X_residual

        # Compute train-test-split and set internal variables
        (X_train, y_train), (X_test, y_test) = train_test_split(
            X_residual_squared,
            y_sigmas_squared,
            train_percentage=train_percentage)
        self.X_train = X_train
        self.y_train = y_train
        self.X_test = X_test
        self.y_test = y_test
        self.has_data = True
示例#2
0
def sliding_window_test():
    X, y = sliding_window(X_dummy, p=3)

    assert X.shape == (6, 3, 1)
    assert y.shape == (2 * (6 - 3), )

    assert np.array_equal(X[0, :, 0], np.array([1, 2, 3]))
    assert np.array_equal(y[0], 4)

    assert np.array_equal(X[5, :, 0], np.array([9, 10, 11]))
    assert np.array_equal(y[4], 11)
示例#3
0
    def preprocess(self, ts, train_percentage):
        if len(ts.shape) == 1:
            ts = ts.reshape(1, len(ts))

        X, y = sliding_window(ts, p=self.p)
        (X_train, y_train), (X_test, y_test) = train_test_split(
            X, y, train_percentage=train_percentage)
        self.X_train = X_train
        self.y_train = y_train
        self.X_test = X_test
        self.y_test = y_test
        self.has_data = True
示例#4
0
def sliding_window_multi_test():
    X, y = sliding_window(X_mult_dummy, p=3)

    assert X.shape == (3, 3, 2)
    assert y.shape == (6 - 3, 2)