def transform(self, X, y=None) -> numpy.array:
"""
Return the fractional differentiation of `X`.
Parameters
----------
X : array_like, shape (n_samples, n_series)
Time-series to perform fractional differentiation.
Raises ValueError if `n_samples < self.window_`.
y : array_like, optional
Ignored.
Returns
-------
fdiff : ``numpy.array``, shape (n_samples, n_series)
The fractional differentiation of `X`.
"""
check_is_fitted(self, ["d_"])
check_array(X)
prototype = Fracdiff(0.5, window=self.window,
mode=self.mode).fit_transform(X)
out = numpy.empty_like(prototype[:, :0])
for i in range(X.shape[1]):
f = Fracdiff(self.d_[i], window=self.window, mode=self.mode)
d = f.fit_transform(X[:, [i]])[-out.shape[0]:]
out = numpy.concatenate((out, d), 1)
return out
def invert_diff(df_forecast, columns_diff_dict):
df_fc = df_forecast.copy()
columns = columns_diff_dict.keys()
for col in columns:
f = Fracdiff(d=-columns_diff_dict[col][0],
window=columns_diff_dict[col][1])
diff = f.fit_transform(df_forecast[col].values.reshape(-1, 1))
df_fc[col] = pd.Series(diff.squeeze())
return df_fc
def test_transform(self, window, mode, precision):
"""
Test if `FracdiffStat.transform` works
for array with n_features > 1.
"""
np.random.seed(42)
X = np.random.randn(100, 10).cumsum(0)
fs = FracdiffStat(window=window, mode=mode, precision=precision).fit(X)
out = fs.transform(X)
exp = np.empty_like(out[:, :0])
for i in range(X.shape[1]):
f = Fracdiff(fs.d_[i], mode=mode, window=window)
exp = np.concatenate((exp, f.fit_transform(X[:, [i]])), 1)
assert_allclose(out, exp)
def test_order(self, window, mode, precision):
np.random.seed(42)
X = np.random.randn(1000, 10).cumsum(0)
fs = FracdiffStat(mode=mode, window=window, precision=precision)
fs.fit(X)
X_st = fs.transform(X)
X_ns = np.empty_like(X_st[:, :0])
for i in range(X.shape[1]):
f = Fracdiff(fs.d_[i] - precision, mode=mode, window=window)
X_ns = np.concatenate((X_ns, f.fit_transform(X[:, [i]])), 1)
for i in range(X.shape[1]):
assert self._is_stat(X_st[:, i])
assert not self._is_stat(X_ns[:, i])
def test_transform(self, d, window, mode):
np.random.seed(42)
X = np.random.randn(100, 200)
fracdiff = Fracdiff(d=d, window=window, mode=mode)
out = fdiff(X, n=d, axis=0, window=window, mode=mode)
assert_array_equal(fracdiff.fit_transform(X), out)
sys.path.append("../..")
from fracdiff import Fracdiff # noqa: E402
def fetch_spx():
return pandas_datareader.data.DataReader(
"^GSPC", "yahoo", "1999-10-01", "2020-09-30"
)["Adj Close"]
if __name__ == "__main__":
s = fetch_spx()
f = Fracdiff(0.5, window=100, mode="valid")
d = f.fit_transform(s.values.reshape(-1, 1)).reshape(-1)
s = s[100 - 1 :]
d = pd.Series(d, index=s.index)
seaborn.set_style("white")
fig, ax_s = plt.subplots(figsize=(16, 8))
ax_d = ax_s.twinx()
plot_s = ax_s.plot(s, color="blue", linewidth=0.6, label="S&P 500 (left)")
plot_d = ax_d.plot(
d,
color="orange",
linewidth=0.6,
label="S&P 500, 0.5th differentiation (right)",
)
plots = plot_s + plot_d
def diff(d):
fracdiff = Fracdiff(d, window=self.window, mode=self.mode)
return fracdiff.fit_transform(x.reshape(-1, 1)).reshape(-1)
