Moving Average using DAX



Let us consider a data set of sales. The objective is to analyze daily sales volume. If we simply build a report that plots the sales amount sliced by day, the result is difficult to decipher.

Following is the time series of day-wise total sales:


Total Sales = SUM(Orders[Sales])

Daily sales volume over 5 years

The graph above shows high variations in total sales value with a horizontal trend pattern.

In statistical data modeling, a common approach is to calculate the average over a certain period higher than just the day level, especially when the time series shows a horizontal pattern over a period. It is also known as Moving Average.

What is a moving average?

The moving averages method uses the average of the most recent k data values in the time series. We call it moving because every time a new observation becomes available for the time series, it replaces the oldest observation in the equation with the latest value and calculates a new average. Thus, the periods over which the average is calculated changes, or moves, with each ensuing period.

Following is an example of 3 periods moving average (k = 3)








Calculating Moving Average using DAX

The objective here is to calculate the moving average of the last 30 days. So, k = 30

Experts recommend creating at least one calendar table in the data model. In the current model, I have created a calendar table and established the relationship:



You may refer to the following article on Calendar Tables.

Then, create a measure with the following code:


Avg Sales (MA 30 days) =
 
//Selecting the date in the range
VAR _LastDate =
  MAX ( dtCalendar[Date] )
 
//Defining the duration to be considered for average calculation(k)
VAR _Duration = 30
 
//Filtering the Calendar Table for the defined range
VAR _CalculationPeriod =
  FILTER (
  ALL ( dtCalendar ),  --Removing any external filters context applied
  AND (
  dtCalendar[Date] > _LastDate - _Duration,  --the range start date
  dtCalendar[Date] <= _LastDate  --the range end date
  )
  )
 
//Calculating the Moving Average
VAR _MovingAverage =
  IF (
  COUNTROWS ( _CalculationPeriod ) >= _Duration, --Condition to check minimum number of days for the calculation.
  CALCULATE (
  AVERAGEX(dtCalendar,[Total Sales]),  --Calculating average of the total sales amount
  _CalculationPeriod
  )
  )
RETURN
  _MovingAverage

The formula first determines the last visible date; in the visual, because the filter context set by the visual is at the date level, it returns the selected date (VAR _LastDate)

Then it creates a set of all the dates between the last date and the last date minus 30 days (VAR _CalculationPeriod).

Finally, the last step is to use this period as a filter in CALCULATE, so that the final AVERAGEX iterates over the 30 days, computing the average of the daily sales.

Following graph presents the output:


The Moving Average line is smoother than the daily sales, which simplifies the trend analysis process.


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