Three Anomaly Detection Methods

Failure is not an option for Sigma Corp. As a lead data scientist for the large conglomerate of energy production companies, it’s up to you to help ensure interruption-free operations by developing a means for detecting anomalies that signal potential problems. Using metrics, including the receiver operating characteristic (ROC) curve and the area under curve (AUC) score, you’ll evaluate anomaly detection algorithms. You’ll build a z-score anomaly detection algorithm, which focuses on a single feature and provides a simple benchmark, and you’ll apply it to a dataset to establish a reference for comparison. When you’re finished, you’ll have a firm grasp of z-score anomaly detection, classification error categories, and evaluating anomaly detection algorithms.

Preventing operation failures and interruptions is mission-critical at Sigma Corp. The large conglomerate of energy production companies has recently implemented a z-score anomaly detection algorithm that focuses on a single feature. Now that the algorithm has proved its value, members of Sigma have requested additional algorithms that are just as simple to use, but that can handle multidimensional data. As a lead data scientist at Sigma, you’ll implement the Mahalanobis distance (MD) method and the principal component analysis (PCA) method as you build anomaly detection algorithms for multidimensional data. To gauge the performance of your algorithms, you’ll test them against a benchmark dataset as well as synthetic anomalies generated by your own algorithms. When you’re done, you’ll have firsthand experience building anomaly detection algorithms for multidimensional datasets as well as testing anomaly detection algorithms against both benchmark datasets and synthetic anomalies.

Sigma Corp, a large conglomerate of energy production companies, has recently implemented anomaly detection algorithms and is generally pleased with their performance. However, analysts report that not all anomalies are being identified and the algorithms are too slow at times. As a lead data scientist at Sigma, it’s up to you to address these concerns. To increase the robustness of the algorithms, you’ll implement and optimize the probability-based Empirical Cumulative distribution-based Outlier Detection (ECOD) method, an alternative to statistical methods. You’ll benchmark the ECOD method in order to compare its performance with the statistical MD and PCA methods Sigma is currently using. When you’re finished, you’ll have firsthand experience implementing the highly efficient ECOD method to detect anomalies in multidimensional data.

Red alert! One of the energy production companies managed by Sigma Corp has suffered an outage. An investigation has led to the conclusion that the facility’s anomaly detection mechanism failed to detect early signals due to a sudden change in the distribution of the analyzed data. As a lead data scientist at Sigma, you’ll build an Isolation Forest algorithm, which is less likely than the Empirical Cumulative distribution-based Outlier Detection (ECOD) method to fail in such scenarios. To gauge how robust your method is, you’ll benchmark your algorithms against adversarial scenarios, synthetic anomalies, and standard datasets. When you’re done, you’ll have practical experience creating, using, and testing the Isolation Forest algorithm as an effective alternative to ECOD in circumstances where the data distribution changes.