AI Anomaly Detection for Time Series: From Statistical to Deep Learning Approaches
Isolation Forest, LSTM Autoencoders, and production anomaly detection systems
AI Anomaly Detection for Time Series: From Statistical to Deep Learning Approaches
Isolation Forest, LSTM Autoencoders, and production anomaly detection systems
Build production anomaly detection systems for time series data using statistical methods, isolation forest, LSTM autoencoders, and modern time series foundation models for infrastructure and IoT monitoring.
Time series anomaly detection is critical for infrastructure monitoring, IoT, financial fraud, and predictive maintenance. Approaches by complexity: 1) Statistical baseline: Z-score, IQR outlier detection, ARIMA residual analysis. Fast, interpretable, good for simple anomalies. 2) Isolation Forest: random forest for outlier detection, works well for multidimensional data, O(n*log(n)). from sklearn.ensemble import IsolationForest; model = IsolationForest(contamination=0.1); model.fit(X_train); anomalies = model.predict(X_test) == -1. 3) LSTM Autoencoder: learns normal patterns, anomalies have high reconstruction error. encoder-decoder architecture trained on normal data only. Threshold reconstruction error at 95th percentile of training errors. 4) Seasonal-Trend decomposition: STL decomposition separates trend + seasonality + residual, detect anomalies in residual component. Handles time-varying baselines. 5) Foundation models: Moirai (Salesforce), TimeGPT, Lag-Llama - pre-trained on massive time series datasets, zero-shot or few-shot anomaly detection. Production system: streaming ingestion -> feature extraction (rolling statistics, spectral features) -> ensemble of detectors (reduce false positives) -> confidence scoring -> alert with context. Alert fatigue prevention: require N consecutive anomalies before alerting, group related anomalies, suppress during known maintenance windows.
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