What transformation can be used to handle historical trends in data?

The computeRelative transformation is particularly valuable for addressing historical trends in data because it allows for the normalization of values relative to a specific point in time or a baseline value. By using this transformation, analysts can assess how current data points compare to historical data, enabling them to identify trends, patterns, and significant changes over time.

This method essentially quantifies changes or growth rates, making it easier to understand how various metrics have evolved. For example, if you are looking to measure sales growth over multiple periods, computeRelative transformation can provide a clear view of performance relative to previous sales, highlighting whether an increase or decrease occurred.

In contrast, other transformations like delta transformation might focus more on changes in absolute values rather than providing insights in relation to an average or previous value. The append transformation typically involves adding data without providing comparative context, and the augment transformation might enhance existing data but doesn't explicitly address handling historical trends directly. Thus, computeRelative stands out as the most effective choice for this particular scenario.