1. Point anomalies: A single instance of data is anomalous if it's too far off from the rest. Business use case: Detecting credit card fraud based on "amount spent."
2. Contextual anomalies: The abnormality is context specific. This type of anomaly is common in time-series data. Business use case: Spending $100 on food every day during the holiday season is normal, but may be odd otherwise.
3. Collective anomalies: A set of data instances collectively helps in detecting anomalies. Business use case: Someone is trying to copy data form a remote machine to a local host unexpectedly, an anomaly that would be flagged as a potential cyber attack.
Intrusion detection (system security, malware), production system monitoring, or monitoring for network traffic surges and drops.
Real time fraud detection (credit cards, insurance, etc.), stock market analysis, early detection of insider trading.
For predictive maintenance or service fraud detection.
Condition monitoring, including seizure or tumor detection.
Used for detection of unusual images from surveillance.
monitor and detect anomalies on price of products as listed on the website enables the business teams to proactively identify and prevent revenue leakages.