What does the double exponential smoothing model focus on?

The double exponential smoothing model is particularly designed to account for trends in time series data. It does this by applying two smoothing constants: one for the level of the series and another for the trend. This dual approach allows the model not only to smooth out random fluctuations in historical data but also to capture and project the underlying trend over time.

By focusing on trends, the double exponential smoothing model is able to generate more accurate forecasts in situations where data shows a consistent upward or downward movement rather than mere randomness. This makes it especially valuable in demand forecasting and other applications where recognizing trends can significantly enhance predictive accuracy.

In contrast, options that suggest focusing solely on averages, recent data, or only accuracy overlook the model's essential capability to adapt to and project trends in the dataset, which is central to its utility in forecasting.