There are various approaches for identifying outliers in time series data. One typical strategy is to employ statistical approaches like the z-score or modified z-score method, in which data points that exceed a given threshold are marked as outliers. Another technique is to employ strong statistical metrics such as median absolute deviation (MAD) or Tukey's fences. Additionally, time series-specific procedures such as seasonal decomposition or residual analysis can aid in detecting anomalous trends. Visualisation approaches like as box plots, scatter plots, and time series plots with superimposed threshold lines can help identify outliers. Finally, machine learning methods such as Isolation Forest or One-Class SVM can be useful for detecting outliers in time series data.

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