SQL and Python: alerts from predictions

The blog post discusses using complex time-series models for anomaly detection in data, beyond simple statistical methods like z-scores. By employing Python libraries such as Prophet or statsmodels, users can predict control limits using pre-coded models without needing to build from scratch. The post provides an example using historic New York taxi trip data to forecast future data and generate alerts for anomalies. The process involves extracting time-series data, fitting a model, generating predictions, and creating a Data Source of these predictions in Tinybird. An alerts system is then set up using an API Endpoint to identify days with unexpected data, thus enabling real-time operational adjustments. This approach highlights the flexibility of integrating SQL-based analysis with advanced modeling techniques to enhance real-time data monitoring and anomaly detection.

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