A Time Series Machine Learning Model for Canary Deployments

The SAX HMM machine learning model effectively identifies dissimilarities in time series data for canary analysis, demonstrating a 75% detection rate at m_max=5 and peaking at 93% with higher m_max values, thereby enhancing deployment reliability and risk assessment. The model is tested using a synthetic dataset inspired by the UCI Synthetic Control dataset, where time series data are analyzed to determine similarity or dissimilarity based on inferred deviation ranges. The methodology involves generating datasets with varying ranges of the parameter m, with each dataset comprising 30 time series, leading to 900 comparisons to detect dissimilarities. The results indicate that the detection rate of dissimilarities increases with m_max and stabilizes around 93%, confirming the model's effectiveness for canary analysis applications.

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