anomaly-detector

Real-time anomaly detection monitors data streams for abnormal patterns in sales, traffic, conversion, and inventory metrics, triggering automated alerts when irregularities are identified. It applies statistical and machine learning methods to enable immediate notification of sudden spikes or drops.

Anomaly detection methods for e-commerce conversion monitoring include statistical rules like 3σ, Z-score, and IQR, alongside machine learning models such as isolation forests, LSTM, and autoencoders. These configurable approaches identify sudden funnel irregularities and traffic drops.

Yes, root-cause analysis is supported for sudden traffic drops and sales spikes through structured anomaly reports. These reports correlate abnormal patterns across monitored business metrics to speed up remediation by identifying underlying factors triggering the alert.

Automated alerting for abnormal inventory patterns is configured by defining thresholds for monitored data streams. The alerting pipeline triggers notifications when inventory metrics deviate from historical baselines, enabling real-time monitoring of stock irregularities.

Use Z-score, 3σ, or IQR for baseline statistical threshold monitoring of simple metric deviations. Apply isolation forests, LSTM, or autoencoders when detecting complex, non-linear abnormal patterns in high-dimensional business metrics like sales and traffic data.

Yes, anomaly detection supports historical analysis of e-commerce metrics by evaluating past data streams alongside real-time monitoring. This historical context helps identify abnormal patterns in sales, traffic, and inventory over time and improves root-cause analysis accuracy.