Implement Trend, Outlier, Bias, and Nelson Rules Detection in _alarms.py

[Reinaldo-Kn]

As mentioned in the issue #39

New Functions in _alarms.py

This module introduces several functions designed to detect trends, outliers, bias, and anomalies in time series data. Each function operates on array-like inputs and returns alarm indicators based on specific criteria.

• detectTrend(data, window_size=5)

Detects the trend in the provided data using a moving average.

Parameters:

    data: array-like
        The input data to be analyzed.
    window_size: int, optional
        The size of the moving window to calculate the trend. Default is 5.

Returns:

    alarm: ndarray
        An array indicating the trend (1 for positive trend, -1 for negative trend, 0 for no trend).

• detectMovingWindowOutlier(data, window_size=10, count_limit=1)

Detects outliers in a moving window of the data.

Parameters:

    data: array-like
        The input data to be analyzed.
    window_size: int, optional
        The size of the moving window to analyze. Default is 10.
    count_limit: int, optional
        The maximum number of outliers allowed within the window. Default is 1.

Returns:

    alarm: ndarray
        An array indicating if the count of outliers exceeds the count_limit within each window (1 for alarm, 0 for no alarm).

• detectBias(data, expected_value, threshold=0.1)

Detects bias in the data by comparing the mean to an expected value.

Parameters:

    data: array-like
        The input data to be analyzed.
    expected_value: float
        The expected mean value to compare against.
    threshold: float, optional
        The threshold for deviation from the expected value. Default is 0.1.

Returns:

    alarm: ndarray
        An array indicating if the bias exceeds the threshold (1 for alarm, 0 for no alarm).

• detectNelsonRules(data, threshold=1)

Detects anomalies in the data based on Nelson Rules 1, 2, and 3.

Parameters:

    data: array-like
        The input data to be analyzed.
    threshold: float, optional
        The multiplier for the standard deviation in anomaly detection. Default is 1.

Returns:

    alarms: dict
        A dictionary with alarms for each rule (1 for alarm, 0 for no alarm):
            rule_1: Alarms for rule 1.
            rule_2: Alarms for rule 2.
            rule_3: Alarms for rule 3.

You can view the new functions in Colab