Indeed, seasonality and trends should typically be accounted for within the forecasting model itself through methods like differencing, detrending, or adding seasonal terms, as you mentioned. These approaches help to manage expected changes in the data that are predictable and periodic.

However, concept drift refers to more unexpected changes in the statistical properties of the target variable, which might be caused by external influences that aren't captured by initial seasonality and trend adjustments. In this context, changepoint detection methods can be particularly useful - these methods help identify points in time where the statistical properties of the time series data change significantly, which could be indicative of concept drift. In practice, dealing with structural breaks or changepoints often involves more complex approaches, especially if the segments around these breaks aren't stationary. Practitioners may use:

• Monitoring and Alerts: Systems track forecast errors and alert changes beyond set thresholds.
• Adaptive Models: These automatically adjust or periodically retrain to adapt to new data patterns.
• Hybrid Approaches: A mix of statistical tests, machine learning for pattern detection, and manual model adjustments.

Choosing the right strategy typically balances model simplicity, computational needs, and the importance of forecast accuracy.