RADAR.time_series.preprocessing

Submodules

RADAR.time_series.preprocessing.preprocessing_ts module

class RADAR.time_series.preprocessing.preprocessing_ts.FilterPreprocessing(filter_func=numpy.mean, kernel_size=3, **kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
inverse_transform(X)[source]
transform(X)[source]

Apply filtering using a moving average kernel.

Input: - X: numpy.ndarray or pandas.Series, representing a one-dimensional

time series. If a DataFrame is provided, each column will be processed independently.

Output: - numpy.ndarray or pandas.Series with the filtered series, using

a moving average filter with the specified kernel size.

class RADAR.time_series.preprocessing.preprocessing_ts.InterpolationPreprocessing(method='linear', **kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
inverse_transform(X)[source]
transform(X)[source]

Apply interpolation to fill missing values.

Input: - X: pandas.DataFrame or pandas.Series, where rows are time steps

and columns may contain missing values that need to be interpolated.

Output: - pandas.DataFrame or pandas.Series with interpolated values, using

the specified interpolation method (e.g., ‘linear’, ‘polynomial’).

class RADAR.time_series.preprocessing.preprocessing_ts.MinMaxScalerPreprocessing(**kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
fit_transform(X)[source]
inverse_transform(X)[source]
transform(X)[source]
class RADAR.time_series.preprocessing.preprocessing_ts.NormalizerPreprocessing(**kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
fit_transform(X)[source]
inverse_transform(X)[source]
transform(X)[source]
class RADAR.time_series.preprocessing.preprocessing_ts.OneHotEncoderPreprocessing(columns=None, **kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
fit_transform(X)[source]
inverse_transform(X)[source]
transform(X)[source]
class RADAR.time_series.preprocessing.preprocessing_ts.RobustScalerPreprocessing(**kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
fit_transform(X)[source]
inverse_transform(X)[source]
transform(X)[source]
class RADAR.time_series.preprocessing.preprocessing_ts.RollingMeanPreprocessing(window=3, **kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
inverse_transform(X)[source]
transform(X)[source]

Apply rolling mean transformation.

Input: - X: pandas.DataFrame or pandas.Series, where rows are time steps

and columns are the different time series.

Output: - pandas.DataFrame or pandas.Series with rolling mean applied over

the specified window.

class RADAR.time_series.preprocessing.preprocessing_ts.StandardScalerPreprocessing(**kwargs)[source]

Bases: BasePreprocessing

fit(X)[source]
fit_transform(X)[source]
inverse_transform(X)[source]
transform(X)[source]

Module contents