Statistical methods are a popular approach for detecting anomalies in time series data. These methods involve analyzing the statistical properties of the data to identify unusual patterns or outliers. Here are some commonly used statistical methods for anomaly detection in time series data:

    1 Z-score: This method involves calculating the number of standard deviations a data point is away from the mean of the data set. Data points that fall outside a certain number of standard deviations are considered anomalous.

    2 Grubbs' Test: This method is used to detect a single outlier in a univariate dataset. It works by calculating a test statistic that measures the distance between the suspected outlier and the mean of the data set.

    3 Boxplot Method: This method involves dividing the data into quartiles and then plotting a box around the middle two quartiles. Data points that fall outside the whiskers of the boxplot are considered anomalous.

    4 Percentiles: This method involves identifying a threshold value beyond which data points are considered anomalous. The threshold value is often based on a certain percentile of the data set, such as the 99th percentile.

These statistical methods are simple to implement and can provide quick insights into the anomalies present in the data. However, they are often sensitive to the assumptions made about the underlying distribution of the data, and may not work well in cases where the data is non-Gaussian or has complex patterns. Therefore, it is important to consider the limitations of these methods and explore other techniques if needed.
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