imputegap.recovery.manager.TimeSeries

class imputegap.recovery.manager.TimeSeries(verbose=True)

Class for managing and manipulating time series data.

This class allows importing, normalizing, and visualizing time series datasets. It also provides methods to contaminate the datasets with missing values and plot results.

Methods

__init__() :

Initializes the TimeSeries object.

import_matrix(data=None) :

Imports a matrix of time series data.

load_series(data, nbr_series=None, nbr_val=None, header=False, normalizer=”z_score”, replace_nan=False, reverse=False, verbose=True):

Loads time series data from a file or predefined dataset.

print(limit=10, view_by_series=False) :

Prints a limited number of time series from the dataset.

print_results(metrics, algorithm=””) :

Prints the results of the imputation process.

normalize(normalizer=”z_score”, data=None, verbose=True):

Normalizes the time series dataset.

plot(input_data, incomp_data=None, recov_data=None, max_series=None, max_values=None, size=(16, 8), save_path=””, display=True) :

Plots the time series data, including raw, contaminated, or imputed data.

__init__(verbose=True)

Initialize the TimeSeries object.

The class works with time series datasets, where each series is separated by space, and values are separated by newline characters.

IMPORT FORMAT : (Values,Series) : series are seperated by “SPACE” et values by “n”

Methods

__init__([verbose])	Initialize the TimeSeries object.
import_matrix([data])	Imports a matrix of time series data.
load_series(data[, nbr_series, nbr_val, ...])	Loads time series data from a file or predefined dataset.
normalize([normalizer, data, verbose])	Normalize the time series dataset.
plot(input_data[, incomp_data, recov_data, ...])	Plot the time series data, including raw, contaminated, or imputed data.
print([nbr_val, nbr_series, view_by_series])	Prints a limited number of time series from the dataset.
print_results(metrics[, algorithm, text])	Prints the results of the imputation process.