Which of the following data actions would likely assist in eliminating irrelevant data points that could mislead forecasts?

Data cleansing is the process of identifying and correcting inaccuracies or inconsistencies in data to improve its quality. This action is crucial when it comes to preparing data for analysis or forecasting, as irrelevant data points can significantly distort the results. By cleansing the data, analysts remove outliers, duplicates, and erroneous entries, which in return leads to more reliable and valid forecasts.

For instance, if a dataset contains records with erroneous values or outliers that don't reflect true measurements, these could skew the results of any predictive modeling or forecasting conducted later. Therefore, data cleansing directly addresses the issue of irrelevant data points, ensuring that the data used for analysis is of high quality, thus enhancing the accuracy of forecasts.

The other options may contribute to different aspects of data analysis, but they don't specifically target the elimination of irrelevant data points. Currency conversion pertains to adjusting values to a common currency, which does not address data quality. Time series forecasting uses historical data to make predictions about future data trends but requires accurate data as input, making it dependent on cleaning processes beforehand. Regression analysis helps to understand the relationships among variables but isn't focused on filtering out irrelevant data points.