timeseriesqualitycheck 0.0.2

timeseriesqualitycheckis a Python package designed to assess the quality of time-series data. It provides a straightforward way to evaluate the integrity and cleanliness of time-series datasets by analyzing their Time Pattern Cohesion Score (TPCS) and Signal-to-Noise Ratio (SNR)

timeseriesqualitycheck

timeseriesqualitycheck is a Python package designed to assess the quality of time-series data. It provides a straightforward way to evaluate the integrity and cleanliness of time-series datasets by analyzing their Time Pattern Cohesion Score (TPCS) and Signal-to-Noise Ratio (SNR).

Installation

To install timeseriesqualitycheck, simply use pip:

pip install timeseriesqualitycheck

check_quality Function

• check_quality requires a signal input in pandas DataFrame format, with at least two columns:

  • y: column where the values of the signal are stored.
  • ds: column where the date information is stored.

• The END_OF_TIME parameter is useful when we have extra information about the time period for the signal. For example, the signal may have values until May but should also include values for April. The END_OF_TIME parameter helps determine possible missing values.

• The MAX_LEN_MONTHS parameter works in a similar way to the END_OF_TIME parameter. However, its purpose is to gauge the existence of missing values from the beginning of the defined data gathering period.

• The function returns a dictionary of values:

  cleaning_score_data_dict = {
      "TPC_features": TPC_features,
      "TPC_score": TPC_score,
      "SNR_features": SNR_features,
      "SNR_score": SNR_score,
      "cleaning_score_weights": cleaning_score_weights,
      "cleaning_score": cleaning_score,
  }
  

  You can access any value you need; the final output key is "cleaning_score".

Usage

import pandas as pd
from timeseriesqualitycheck import check_quality

END_OF_TIME= pd.to_datetime("2021-05-01")
MAX_LEN_MONTHS=13

list_of_timestamps= ["2020-05-01","2020-06-01","2020-07-01","2020-08-01","2020-09-01","2020-10-01","2020-11-01","2020-12-01","2021-01-01", "2021-03-01","2021-04-01","2021-05-01" ]
#notice that "2021-02-01" is missing
list_of_timestamps= [pd.to_datetime(e) for  e in list_of_timestamps]

signal_values_for_timestamps=[20,30,40, 50,600, 70, 80, 70, 60, 50,40, 30 ]
#notice that we have an outlier(600) value


dict = {'ds': list_of_timestamps, 'y': signal_values_for_timestamps} 
df = pd.DataFrame(dict)

quality_report = check_quality(df, 12, '2023-12-31')
print(quality_report)

Description

The check_quality function evaluates the quality of a time-series signal. It analyzes the signal for pattern consistency, contiguity, and noise levels to produce a comprehensive quality score.

Syntax

timeseriesqualitycheck.check_quality(signal, MAX_LEN_MONTHS, END_OF_TIME, snr_limit=3.5)

Parameters

• signal (pd.DataFrame): A pandas DataFrame containing the time-series data with 'y' and 'ds' columns.
• MAX_LEN_MONTHS (int): The maximum length of the time series in months.
• END_OF_TIME (datetime): The end date for the time series data.
• snr_limit (float, optional): The threshold for the signal-to-noise ratio. Default is 3.5.

Returns

• dict: A dictionary containing the cleanliness score, TPC and SNR features, and detailed scores.

Contributing

Contributions to timeseriesqualitycheck are welcome. Please ensure that your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License.

Additional Notes on check_quality Function: