Anomaly Detection TS is a technique for identifying unusual or unexpected behavior in time series data, such as sudden spikes, drops, or structural changes over time. It models normal temporal dynamics using statistical, signal-processing, or machine-learning methods, then flags observations or segments that deviate beyond learned thresholds or confidence bounds. It can run in batch mode on historical data or in streaming mode for real-time monitoring of systems and processes.

Managed AutoML platforms package feature engineering, model selection, training, deployment, and monitoring into a guided workflow so teams can ship predictive models quickly without owning a full bespoke ML stack.

Simulation-Optimization combines computational simulation models with optimization algorithms to find optimal decisions under uncertainty and complex constraints. It runs many simulation scenarios to evaluate candidate solutions, using techniques like genetic algorithms, Bayesian optimization, or reinforcement learning.