Pre-processingΒΆ

ImputeGAP enables time series normalization as a preprocessing step prior to imputation. Users can select from two normalization techniques to standardize their data distribution.

  • Z-score normalization: Standardizes data by subtracting the mean and dividing by the standard deviation, ensuring a mean of 0 and a standard deviation of 1.

  • Min-max normalization: Scales data to a fixed range, typically [0,1], by adjusting values based on the minimum and maximum in the dataset.

You can access the API documentation at the following link: (normalize).

from imputegap.recovery.manager import TimeSeries
from imputegap.tools import utils

# initialize the TimeSeries() object
ts = TimeSeries()

# load the timeseries from file or from the code
ts.load_series(utils.search_path("eeg-alcohol"), nbr_series=10, nbr_val=200)
ts.normalize(normalizer="z_score")