td-change-point

Detect structural breaks in time series data by applying the TD_CHANGE_POINT function via Teradata's Unbounded Array Framework (UAF). This pinpoints significant shifts in time series behavior for applications like forecasting, anomaly detection, and process monitoring.

Change point detection in time series analysis identifies critical points where underlying patterns or structures change. It analyzes regime changes and structural breaks in high-dimensional, large-scale datasets to enable proactive analysis and intervention.

Yes, change point analysis requires Teradata Vantage with the Unbounded Array Framework (UAF) enabled. You must also have the appropriate licensing configured to execute the TD_CHANGE_POINT function on your datasets.

Yes, you can use UAF to find regime changes in industrial sensor data. By analyzing sensor readings, it detects when a machine's performance characteristics change, indicating potential maintenance needs or operational shifts.

The best way to monitor high-dimensional datasets for structural shifts is using the TD_CHANGE_POINT function in Teradata's UAF. It processes large-scale datasets to pinpoint significant shifts in time series behavior for anomaly detection.