In financial modeling, which data action is typically used to examine how variables interact over time?

The answer is A, Time series forecasting, because this data action is designed specifically to analyze historical data points collected over time in order to predict future values and understand the relationships between different variables. Financial modeling often involves examining trends, seasonal patterns, and cyclic behavior in financial data, which are core components of time series analysis. Utilizing techniques like autoregression, moving averages, or exponential smoothing, time series forecasting allows analysts to illustrate how changes in one variable can impact others through temporal analysis. This capability is fundamental in financial contexts, such as budget forecasting or expense planning, where understanding the dynamics over time is critical for making informed decisions.

The other options serve different purposes. Anomaly detection primarily focuses on identifying unusual patterns or outliers in data, which is not aimed at understanding variable interactions over time. Currency conversion is concerned with translating monetary values from one currency into another, relevant for financial analysis but not directly related to the temporal interaction of variables. Data cleansing involves preparing and correcting data for analysis, which is a necessary step in data management but does not engage with the temporal elements like time series forecasting does.