mlfinlab.features.lowess
Module implements Locally Weighted Scatterplot Smoothing (LOWESS) as presented by Marcos Lopez de Prado and Riccardo Rebonato in the following paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2422183
Module Contents
Classes
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Locally Weighted Scatterplot Smoothing (LOWESS), is a non-parametric regression |
- class LOWESS(time_series: pandas.DataFrame, fraction: float | None = 0.5)
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Bases:
mlfinlab.features.base_noise_reduction.NoiseReductionMethodLocally Weighted Scatterplot Smoothing (LOWESS), is a non-parametric regression technique to obtain a smooth line through a scatterplot.
This class implementation applies LOWESS to a noisy time-series signal to obtain a smoother signal as a feature.
- property fraction: float
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Returns fraction of datapoints used for estimation by LOWESS.
- Returns:
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(float) Fraction of datapoints.
- property dataframe: pandas.DataFrame
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Returns dataframe with original time series and relevant signals generated by the noise reduction method.
- Returns:
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(pd.DataFrame) Pandas DataFrame with relevant signals.
- property signal: numpy.array
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Returns main signal generated from noise reduction method.
- Returns:
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(np.array) Main signal generated from noise reduction method.
- __slots__ = ()
- set_fraction(fraction: float)
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Set LOWESS fraction value.
- Parameters:
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fraction – (int) Fraction amount to use for estimations.
- generate_signal() numpy.array
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Generate signal by applying a LOWESS fit.
- Returns:
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(np.array) Smooth signal.