JSTOR ( July 2020) ( Learn how and when to remove this template message).Unsourced material may be challenged and removed. Please help improve this article by adding citations to reliable sources. The most popular frequencies are annual, quarterly, monthly and semi-monthly.This article needs additional citations for verification.

To protect against end-date creep, we provide pre-scheduled refreshes of data according to a timetable of your choice. For example, if your last delivery covered the period to June 1, 2015, end-date creep means that by May 10 you would only have 21 days of forward data.

Keep in mind that you will also receive pre-scheduled deliveries to protect against “end-date creep.” This is the phenomenon whereby the end-date of your window moves closer with the passage of time. By contrast, when issuing an update to Static window customers, we normally deliver data only for the affected centers. This is because, in order to ensure that you have the same end date for every financial center, all deliveries must consist of a full set of fresh data for every financial center that you license.

Rolling windows entail a bigger data volume (except with the Deltas layout).

All windows under two years must be Rolling.

For example, a customer who gets a 5-year Static window would receive data for the period Januto Decemfor deliveries made on both May 4 and May 18, 2015. The Static window rolls forward once a year in late January or early February.

A Static window, on the other hand, always starts on January 1 of one year and ends on December 31 of another.

If this customer receives another delivery on May 18, 2015, the data would cover to May 18, 2020. For example, a customer with a 5-year Rolling window who gets a delivery on would receive data covering the period from to May 4, 2020.

A Rolling window is expressed relative to the delivery date and automatically shifts forward with the passage of time.

Two types of data window–Rolling and Static For the forward-looking data, your window of time can be expressed in days, months or years. We can deliver forward data out to the year 2200, as well as extensive history.

Windows vs mac for data science