Super-fast fractional differentiation.


Keywords
finance, python, pytorch, time-series
License
BSD-3-Clause
Install
pip install fracdiff==0.8.0

Documentation

Fracdiff: Super-fast Fractional Differentiation

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Documentation

Fracdiff performs fractional differentiation of time-series, a la "Advances in Financial Machine Learning" by M. Prado. Fractional differentiation processes time-series to a stationary one while preserving memory in the original time-series. Fracdiff features super-fast computation and scikit-learn compatible API.

spx

What is fractional differentiation?

See M. L. Prado, "Advances in Financial Machine Learning".

Installation

pip install fracdiff

Features

Functionalities

Speed

Fracdiff is blazingly fast.

The following graphs show that Fracdiff computes fractional differentiation much faster than the "official" implementation.

It is especially noteworthy that execution time does not increase significantly as the number of time-steps (n_samples) increases, thanks to NumPy engine.

time

The following tables of execution times (in unit of ms) show that Fracdiff can be ~10000 times faster than the "official" implementation.

n_samples fracdiff official
100 0.675 +-0.086 20.008 +-1.472
1000 5.081 +-0.426 135.613 +-3.415
10000 50.644 +-0.574 1310.033 +-17.708
100000 519.969 +-8.166 13113.457 +-105.274
n_features fracdiff official
1 5.081 +-0.426 135.613 +-3.415
10 6.146 +-0.247 1350.161 +-15.195
100 6.903 +-0.654 13675.023 +-193.960
1000 13.783 +-0.700 136610.030 +-540.572

(Run on Ubuntu 20.04, Intel(R) Xeon(R) CPU E5-2673 v3 @ 2.40GHz. See fracdiff/benchmark for details.)

How to use

Open In Colab

Fractional differentiation

A function fdiff calculates fractional differentiation. This is an extension of numpy.diff to a fractional order.

import numpy as np
from fracdiff import fdiff

a = np.array([1, 2, 4, 7, 0])
fdiff(a, 0.5)
# array([ 1.       ,  1.5      ,  2.875    ,  4.6875   , -4.1640625])
np.array_equal(fdiff(a, n=1), np.diff(a, n=1))
# True

a = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
fdiff(a, 0.5, axis=0)
# array([[ 1. ,  3. ,  6. , 10. ],
#        [-0.5,  3.5,  3. ,  3. ]])
fdiff(a, 0.5, axis=-1)
# array([[1.    , 2.5   , 4.375 , 6.5625],
#        [0.    , 5.    , 3.5   , 4.375 ]])

Scikit-learn API

Preprocessing by fractional differentiation

A transformer class Fracdiff performs fractional differentiation by its method transform.

from fracdiff.sklearn import Fracdiff

X = ...  # 2d time-series with shape (n_samples, n_features)

f = Fracdiff(0.5)
X = f.fit_transform(X)

For example, 0.5th differentiation of S&P 500 historical price looks like this:

spx

Fracdiff is compatible with scikit-learn API. One can imcorporate it into a pipeline.

from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

X, y = ...  # Dataset

pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('fracdiff', Fracdiff(0.5)),
    ('regressor', LinearRegression()),
])
pipeline.fit(X, y)

Fractional differentiation while preserving memory

A transformer class FracdiffStat finds the minumum order of fractional differentiation that makes time-series stationary. Differentiated time-series with this order is obtained by subsequently applying transform method. This series is interpreted as a stationary time-series keeping the maximum memory of the original time-series.

from fracdiff.sklearn import FracdiffStat

X = ...  # 2d time-series with shape (n_samples, n_features)

f = FracdiffStat()
X = f.fit_transform(X)
f.d_
# array([0.71875 , 0.609375, 0.515625])

The result for Nikkei 225 index historical price looks like this:

nky

PyTorch API

One can fracdiff a PyTorch tensor. One can enjoy strong GPU acceleration.

from fracdiff.torch import fdiff

input = torch.tensor(...)
output = fdiff(input, 0.5)
from fracdiff.torch import Fracdiff

module = Fracdiff(0.5)
module
# Fracdiff(0.5, dim=-1, window=10, mode='same')

input = torch.tensor(...)
output = module(input)

More Examples

Open In Colab

More examples are provided here.

Open In Colab

Example solutions of exercises in Section 5 of "Advances in Financial Machine Learning" are provided here.

Contributing

Any contributions are more than welcome.

The maintainer (simaki) is not making further enhancements and appreciates pull requests to make them. See Issue for proposed features. Please take a look at CONTRIBUTING.md before creating a pull request.

References