Anomaly Detection Engine

Applies parametric methods to establish control limits based on historical data distributions. The engine calculates moving averages and standard deviations to flag observations that fall outside acceptable thresholds.

• Control Charts: Shewhart charts monitor process stability over time
• Z-Score Analysis: Measures how many standard deviations a data point is from the mean
• Grubbs' Test: Identifies single outliers in normally distributed datasets

Best suited for stable, predictable metrics like warehouse throughput or transit time consistency.

An unsupervised ensemble method that isolates anomalies by recursively partitioning data. Anomalous points require fewer random splits to be isolated from normal observations, making them computationally efficient.

• Path Length: Shorter average path lengths indicate higher anomaly scores
• Sub-sampling: Builds trees on random data subsets for scalability
• Linear Time Complexity: Handles high-dimensional logistics data efficiently

Effective for detecting fraudulent transactions or unusual supplier behavior in large datasets.

A deep learning architecture that learns compressed representations of normal time-series patterns. The model reconstructs input sequences and flags periods where reconstruction error exceeds a dynamic threshold.

• Encoder-Decoder Structure: Compresses then reconstructs temporal sequences
• Memory Cells: Capture long-range dependencies in seasonal demand patterns
• Residual Analysis: Quantifies deviation magnitude for severity scoring

Ideal for predictive maintenance and detecting subtle sensor degradation in IoT streams.

A density-based spatial clustering algorithm that identifies anomalies as points in low-density regions. Unlike centroid-based methods, it discovers clusters of arbitrary shape and automatically labels noise.

• Epsilon Neighborhood: Defines the radius for density connectivity
• Minimum Points: Sets the threshold for core point classification
• No Cluster Assignment: Points not belonging to any cluster are flagged as anomalies

Applied to geofence violation detection and route deviation analysis in fleet tracking.

Combines seasonal decomposition with the Generalized Extreme Studentized Deviate test to detect anomalies in time series with strong cyclical patterns. It separates trend, seasonality, and residual components before testing.

• STL Decomposition: Splits series into trend, seasonal, and remainder components
• Iterative Testing: Removes detected anomalies and re-tests residuals
• Robust to Seasonality: Prevents false positives during peak demand periods

Critical for demand forecasting systems monitoring for sudden shifts in ordering patterns.

An adaptive mechanism that continuously recalibrates alert thresholds based on evolving data distributions. It prevents alert fatigue by suppressing false positives during legitimate operational shifts.

• Rolling Window Statistics: Recalculates baselines over configurable time windows
• Contextual Bandits: Uses reinforcement learning to optimize threshold sensitivity
• Feedback Integration: Incorporates operator confirmations to refine models

Ensures the engine maintains high precision as supply chain conditions evolve seasonally.