MDX in Analysis Services: Mastering Time: Moving Averages - Another Approach
September 27, 2004
About the Series ...
This article is a member of the series MDX in Analysis Services. The series is designed to provide hands-on application of the fundamentals of MDX from the perspective of MS SQL Server 2000 Analysis Services; our primary focus is the manipulation of multidimensional data sources, using MDX expressions, in a variety of scenarios designed to meet real-world business intelligence needs.
For more information on the series, as well as the hardware / software requirements to prepare for the tutorials we will undertake, please see the first lesson of this series: MDX Concepts and Navigation.
Note: At the time of writing, Service Pack 3 / 3a updates are assumed for MSSQL Server 2000, MSSQL Server 2000 Analysis Services, and the related Books Online and Samples. Images are from a Windows 2003 Server environment, upon which I have also implemented MS Office 2003, but the steps performed in the articles, together with the views that result, will be quite similar within any environment that supports MSSQL Server 2000 and MSSQL Server 2000 Analysis Services ("Analysis Services" or "MSAS"). The same is generally true, except where differences are specifically noted, when MS Office 2000 and above are used in the environment, with respect to any MS Office components presented in a given article.
In this article, we return to the subject of the previous article in the series, Mastering Time: Introduction to Moving Averages, and examine another approach for achieving "rolling average" aggregations. As we learned in our last article, "rolling," or "moving," averages, form a common business requirement, and involve a the aggregation of an average over a progressively moving window of time periods. While the functions that define the "window" involve the Time dimension in the vast majority of cases, we should be aware that we can apply these functions to members of other dimensional levels, as well.
We stated, in the previous session, that rolling averages are popular in the business community because they have the effect of smoothing values that fluctuate over time; we gave examples of useful applications of moving averages (such as cases where seasonal variations and other volatility factors come into play for a given measure), and then discussed some of their uses. We noted that the assistance moving averages offer in spotting trends (especially helpful in volatile markets, but in many other environments, as well), make moving averages a popular tool among technical analysts, as well as a commonly used contributor to many other technical indicators and overlays.
We broke down the concept of a rolling average, and explained that it is generated simply by calculating the average of the current value, together with the specified number of previous values, typically identified by their placement at time intersects. We noted that MDX affords multiple approaches to generating rolling averages, and we explored one of these through a multi-step practice example.
In this article, we will present a different approach, but will attempt to activate the underlying concept in a similar set of procedural steps. With each step, we will discuss our objectives, as well as the results we obtain. We will: