You are tasked with building a real-time anomaly detection system for a high-volume streaming data pipeline. Describe your approach to selecting an appropriate anomaly detection algorithm, considering factors like data characteristics (e.g., seasonality, trend), computational complexity, and the need for low latency. How would you evaluate the system's performance and handle evolving anomaly patterns over time?

Employ a MECE framework for algorithm selection: 1. Data Characteristics: Analyze seasonality, trend, stationarity, and distribution. For high-volume streaming, prioritize algorithms robust to concept drift. 2. Computational Complexity: Evaluate O(n) for training/inference, memory footprint. Favor online learning or incremental algorithms (e.g., Isolation Forest, One-Class SVM, Prophet for time series). 3. Latency Requirements: Select algorithms with fast inference times (e.g., lightweight neural networks, statistical process control). Evaluate performance using A/B testing, precision-recall curves, and F1-score. Handle evolving patterns via adaptive thresholds, retraining schedules, and ensemble methods with weighted voting.

My approach leverages a CIRCLES framework for system design and algorithm selection. First, I'd characterize the data: volume, velocity, variety (univariate/multivariate), seasonality, trend, and presence of labels. For high-volume streaming, algorithms must be online, incremental, or capable of mini-batch processing. Considering computational complexity and low-latency, I'd prioritize algorithms like Isolation Forest, One-Class SVM, or robust statistical process control methods (e.g., EWMA) for their efficiency. For time-series data with seasonality, Prophet or SARIMA with anomaly detection extensions would be considered. Performance evaluation involves A/B testing, using metrics like precision, recall, F1-score, and AUC-PR, especially given potential class imbalance. To handle evolving anomaly patterns, I'd implement adaptive thresholds, scheduled model retraining (e.g., weekly or bi-weekly), and potentially an ensemble approach with weighted voting or meta-learning to combine diverse models and adapt to concept drift.