I recently wrote about how I wrote an Excel Lambda Moving Average formula. I started with internet searches, which led to formulas which didn’t work or formulas which were too complicated for me to understand. After a brief and amusing foray into ChatGPT, I built myself a workable formula that worked by generating a running sum, subtracting an earlier point’s running sum from the current point’s running sum, and dividing by the number of points. This worked fine for a simple N-point moving average, but it broke down for N-day moving averages where some days in the data set may be missing. And there are probably better ways to do the moving average without calculating running sums.

## Jon’s Previous Moving Average Formula

I developed the following LAMBDA moving average formula, as described in my previous post:

=LAMBDA(datarange,numpoints, LET( runsum,SCAN(,datarange,LAMBDA(a,b,a+b)), BYROW( SEQUENCE(ROWS(datarange)), LAMBDA(x, IF( x<numpoints, NA(), (INDEX(runsum,x)-IF(x=numpoints,0,INDEX(runsum,x-numpoints))) /numpoints) ) ) ) )

Let me partially deconstruct the formula below, showing internal calculations leading to the moving average. The `LAMBDA`

takes the data range and `numpoints`

, the number of points in the moving average, as arguments. The first column contains 25 values in the data range. Column 2 is the element number `x`

of the output array which is created using `BYROW`

. The third column contains the running sum, created using `SCAN`

. The fourth column is the same running sum offset by `numpoints`

. The fifth column shows the moving average calculation, which is the running sum minus the running sum `numpoints`

points ago divided by `numpoints`

. Where `x`

is less than `numpoints`

, the result is #`N/A`

, because there aren’t enough points to divide by `numpoints`

. Where `x`

equals `numpoints`

, zero is subtracted from the running sum, since the running sum of zero points is zero.

Here is how it looks in action. The data range is in B5:B29, the number of points being averaged is in C2, and the moving range formula is entered into cell C5 and spills down to C29. The first several calculated values are #N/A, until we have as many points as we are averaging (the number in C2).

## Improvement #1: CHOOSEROWS

I exchanged several comments on my earlier post with a smart reader named Henk-Jan van Well, who suggested several improvements to the moving average algorithm, starting with `CHOOSEROWS`

.

`CHOOSEROWS`

allows you to select which rows of an array to use. `CHOOSEROWS(array,3)`

returns the 3rd row of that array. `CHOOSEROWS(array,{2,4})`

returns the 2nd and 4th rows. Our approach uses `SEQUENCE`

to generate a list of row numbers to return, and we simply use `AVERAGE`

to, well, average these rows.

=LAMBDA(datarange,numpoints, MAKEARRAY( ROWS(datarange),, LAMBDA(r,c, IF( r<numpoints, NA(), AVERAGE( CHOOSEROWS( datarange, SEQUENCE(numpoints,,1+r-numpoints) ) ) ) ) ) )

Here is a deconstruction of the formula. Again, the `LAMBDA`

moving average takes the data range and `numpoints`

, the number of points in the moving average, as arguments. The data range is in the first column, the output row number `r`

is in the first row (I’m only showing the first 18 points, but you get the idea). For the first 4 points (less than `numpoints`

), the function returns `#N/A`

. After that, `CHOOSEROWS`

selects the indicated values from the data range. These are averaged in the last row, which is returned by the function.

The calculations and chart look the same as above.

## Improvement #2: Calculate Averages for First Points

Henk-Jan also mentioned an averaging scheme used by some econometricians for points that occur before the official number of points to average. I don’t like that definition of averaging early points, but I have an alternative, which simply averages however many points there are. This actually simplifies our formula.

=LAMBDA(datarange,numpoints, MAKEARRAY( ROWS(datarange),, LAMBDA( r,c, AVERAGE( CHOOSEROWS( datarange, SEQUENCE( MIN(r,numpoints),, MAX(1,1+r-numpoints) ) ) ) ) ) )

Here is a deconstruction of the new formula. The `LAMBDA`

takes the data range and `numpoints`

as arguments. The data range is in the first column, the output row number `r`

is in the first row (only showing the first 18 points). `CHOOSEROWS`

selects the indicated values from the data range; note that fewer than numpoints are selected for the first few columns. The selected values are averaged in the last row, which is returned by the function.

The results look the same as before, except that the moving average begins right at the first point.

## Improvement #3: TAKE and DROP

The array functions TAKE and DROP allow you to keep or remove rows and columns from the beginning and end of an array. This is simpler than generating a list of row numbers to pass into CHOOSEROWS to define a moving array to average.

For each row r of the moving average array we create, we `TAKE`

the first r rows, then we `DROP`

all but `numpoints`

from the beginning (and if r is less than `numpoints`

, we don’t `DROP`

any rows).

=LAMBDA(datarange,numpoints, MAKEARRAY(ROWS(datarange),1, LAMBDA(r,c, LET( movingdatarange, DROP( TAKE(datarange,r), MAX(0,r-numpoints) ), AVERAGE(movingdatarange) ) ) ) )

I’ve only deconstructed a few points below. The `LAMBDA`

moving average takes the data range (which is `r`

rows tall) and `numpoints`

as arguments. The function uses TAKE to take the first `r`

rows, then uses DROP to drop the first `r`

–`numpoints`

rows, or zero if `r`

<`numpoints`

. For point 3, the function takes the first 3 rows, then drops zero rows, and returns the average of these three values. For point 10, the function takes the first 10 rows, drops 5 rows (`r`

–`numpoints`

= 10-5), and averages these 5 (`numpoints`

) rows. For point 23, the function takes the first 23 rows, drops 18 rows (`r`

–`numpoints`

= 23-5), and averages these 5 (`numpoints`

) rows.

The result looks the same, with the moving range starting right at the beginning. The LAMBDA formula may be a bit shorter, but it also seems more reliable when the number of points may vary (as when averaging by dates below).

An easier alternative is the following, where we `TAKE`

the first r rows for each array element, then `TAKE`

the last `numpoints`

rows of this. If you try to `TAKE`

more than the number of rows in an array, you simply get the entire array.

=LAMBDA(datarange,numpoints, MAKEARRAY(ROWS(datarange),1, LAMBDA(r,c, LET( movingdatarange, TAKE( TAKE(datarange,r), -numpoints ), AVERAGE(movingdatarange) ) ) ) )

Again I’ve only deconstructed a few points below. The `LAMBDA`

takes the data range (which is `r`

rows tall) and `numpoints`

as arguments. The function uses TAKE to take the first `r`

rows, then uses `TAKE`

again to take the last `numpoints`

rows; if `r`

<`numpoints`

then `TAKE`

only takes as many rows as are available. For point 3, the function takes the first 3 rows, then takes all available rows, and returns the average of these three values. For point 10, the function takes the first 10 rows, takes the last 5 (`numpoints`

) of these rows, and averages these rows. For point 23, the function takes the first 23 rows, then keeps the last 5 (`numpoints`

) of these rows, and averages these rows.

Same results, but the function is a few characters shorter and may be easier to understand.

## Major Enhancement: Moving Average by Date

If a range has values for all dates, then a moving average by date is the same as a moving average by point. A seven-day moving average is a seven-point moving average. But if some dates are missing, then a seven-point moving average will average points from more than seven days. I want to average only data which falls within a seven-day window, which means averaging fewer than seven points if some days are missing. The screenshot below illustrates the problem. There will be a missing value when a measurement is not made on a given day. Note for example, that 1/9/2023 and 1/10/2023 are missing.

Here is the `LAMBDA`

formula, which takes the dates, the data values to be averaged, and `numdays`

, the number of days to average, as inputs.

=LAMBDA(daterange,datarange,numdays, MAKEARRAY(ROWS(datarange),1, LAMBDA(r,c, LET( firstdate, XMATCH( INDEX(daterange,r)-numdays+1, daterange, 1), movingdatarange, DROP( TAKE(datarange,r), MAX(0,firstdate-1) ), AVERAGE(movingdatarange) ) ) ) )

In the first section of the deconstruction below I show row number (tan-shaded), the end date for that average, which is the date for that row number, the allowed start date, which is the end date minus `numdays`

+1, and the actual start date, which is the earliest date on or after the allowed start date. The blue shaded dates show which rows have different allowed and actual start dates.

In the second section of the deconstruction, I show dates (gold-shaded) and values in the first two columns. In the first row is the row number `r`

of the output array (tan-shaded). The remaining columns show the dates that fall between the allowed start date and end date for each row. The green-shaded columns show which columns contain fewer than `numdays`

dates. Not very many of the rows actually average `numdays`

values.

The LAMBDA moving average formula uses the `TAKE`

/`DROP `

approach from the previous section to average the appropriate values.

Below is the output of the formula.

Here is comparison of a 7-point (orange line) and a 7-day (blue line) moving average. The blue shaded cells show where the two are not the same.

This is the case when I track my weight: while traveling or when I wake up too late or am just too busy in the morning to weigh myself, then I don’t have a row for the date I’ve missed. This formula treats such occasions nicely.

## Sample Workbook

You can click on this link to download a workbook that contains these examples: Improved Excel Lambda Moving Average.xlsx.

## More Articles About Dynamic Arrays, LET, and LAMBDA

- ISPRIME Lambda Function
- Lambda Moving Average Formulas
- Excel Lambda Moving Average
- LAMBDA Function to Build Three-Tier Year-Quarter-Month Category Axis Labels
- Dynamic Array Histogram
- Calculate Nice Axis Scales with LET and LAMBDA
- VBA Test for New Excel Functions
- Dynamic Arrays, XLOOKUP, LET – New Excel Features

Peter Bartholomew says

Hi Jon

I tried another approach to the problem, which would be considered totally over-complicated were it not for the fact that I had written the Lambda function Convolveλ a couple of weeks ago.

The function itself is posted on the Microsoft Community since my grasp of GitHub is somewhat tenuous!

https://techcommunity.microsoft.com/t5/excel/would-you-recommend-the-use-of-excel-complex-numbers-where/m-p/3755109#M182986

Jon Peltier says

Hi Peter –

I don’t have the math background to understand your formulas. Could you describe for the layman what it does?

Peter Bartholomew says

Hi Jon.

The saving grace of the convolution formulas is that they can be used without understanding the Fast Fourier Transform that they rely upon; I found them crazily difficult to implement in Excel.

A convolution is something that is used in image processing, along with a multitude of other applications that make modern technology work. What it does is to combine two functions, replacing each point on one curve by an equivalent distribution of values defined by the other function.

In the present case what is required is 5 non-zero values placed about 0, each with a magnitude of 1/5. For softening a photographic image, a smoother distribution would be used. The calculation could be performed by building a ‘sloping’ array of values and summing the columns but, in MRI scanners say, that would require the use of 4096×4096 arrays. The Fast Fourier transforms I used, were first implemented in the 1960s and reduce the calculation to the equivalent of a 4096×12 array, the time saving being important.

Here, one should simply regard the Convolve function as if it were a native Excel function. Understanding it might be interesting but it is not required or even recommended, unless you have a math or electrical engineering background. Even to apply it, there are some catches, for example the way that the smoothing function wraps around, so that a value one might wish to place at -1 actually appears at the far end of the ‘ array (shaded out to show it is not plotted).

I liked the way in which the offset that usually appears in curve averaging disappears (though it can be reintroduced by redefining the spread of point in the smoothing curve to cover 1-5 rather than -2 to +2.

For me, the point of interest was to see whether the functions I had already developed could be applied to your problem. They are more suited to problem sizes exceeding 2500+ points (with a spread of 512 say) but the sample gave me an opportunity to test the ideas. Thank you.

David N says

You could even combine both moving average methods into a single custom LAMBDA by using the date array as an optional argument. So taking the techniques you described above…

=LAMBDA(data_array,num_pts,[date_array],

IF(ISOMITTED(date_array),

MAKEARRAY(ROWS(data_array),1,LAMBDA(r,c,

LET(

set,DROP(TAKE(data_array,r),MAX(0,r-num_pts)),

AVERAGE(set)

)

)),

MAKEARRAY(ROWS(data_array),1,LAMBDA(r,c,

LET(

first_date,XMATCH(INDEX(date_array,r)-num_pts+1,date_array,1),

set,DROP(TAKE(data_array,r),MAX(0,first_date-1)),

AVERAGE(set)

)

))

)

)

Or if the TAKE-TAKE method is preferred over DROP-TAKE for the by point route, then…

=LAMBDA(data_array,num_pts,[date_array],

IF(ISOMITTED(date_array),

MAKEARRAY(ROWS(data_array),1,LAMBDA(r,c,

LET(

set,TAKE(TAKE(data_array,r),-num_pts),

AVERAGE(set)

)

)),

MAKEARRAY(ROWS(data_array),1,LAMBDA(r,c,

LET(

first_date,XMATCH(INDEX(date_array,r)-num_pts+1,date_array,1),

set,DROP(TAKE(data_array,r),MAX(0,first_date-1)),

AVERAGE(set)

)

))

)

)