Excel Rolling Mean of 3 Similar Consecutive Observations - excel

Question

I'm trying to find the rolling mean of time series while ignoring values that do not follow the trend.
x
869
1570
946
0
1136
So, what I would want the result to look like is...
x | y
869 | 0
1570 | 0
946 | 1128.33
3 | 0
1136 | 1217.33 ([1136+1570+946]/3)
900 | 2982 ([946+1136+900]/3)
860 | 2896
The tough part here is if the row I'm on is a trending value I want to take the 3 previous trending values and find them mean of them, but if it's a non-trending value I want it to just zero out. Sometimes I might have to skip 2 or 3 previous lines to get 3 trending values to take the average as well.
So far I've been using array, RC formulas in a VBA macro form, but I'm not sure I could use RC here or if it has to be something else completely. Any help would be greatly appreciated.

Answer 1

I believe I can help you with your problem. First three notes:
1) It appears to me that you are trying to do DCA on smoothed production profiles, ignoring months without a complete record or no data. I'm making this assumption since you mentioned this was time series data but didn't give a sample rate. 2) I've added some extra 'data' for the sake of demo-ing. 3) In your example you shared, the last two values in your 'Y' column it looks like you may have summed but have forgotten to divide.
The solution I came up with has three parts: 1) create a metric to identify 'outliers'; 2) flag 'outliers'; 3) smooth non-flagged data. Let's establish some worksheet infrastructure and say that your production values are in column B and the associated time is in column A as follows:
Part 1) In column 'C', estimate a rough data value based on a trend approximated from two points on either side of your current time step. Subtract the actual value from this approximation. The result will always be positive and quite large for a timestep with little or no production.
=(INTERCEPT(B1:B6,A1:A6)+(A4*SLOPE(B1:B6,A1:A6)))-B4
Part 2) In column 'D', add a condition for when the value computed above is larger than the actual data point. Have it use '0' to identify a point that shouldn't be included in your average. Copy this down to the end of your data as well.
=IF(C4>B4,0,1)
Our sheet now looks like this:
3) Your three element average can now be computed. In the last cell of column 'E', enter the following array formula. You have to accept this formula by pressing ctrl + shift + enter. Once that is done fill the column from bottom to top:
=IFERROR(IF(D17=1,AVERAGE(INDEX(B12:B17,MATCH(2,1/(FIND(1,D12:D17)))),INDEX(B12:B16,MATCH(2,1/(FIND(1,D12:D16)))-COUNTIF(D17,"=0")),INDEX(B12:B15,MATCH(2,1/(FIND(1,D12:D15)))-COUNTIF(D16:D17,"=0"))),0),"")
This takes averages the most recent three values and allows for a skip of up to three time steps of outlier data per your problem statement. For an idea of how the completed sheet looks:
This was a fun challenge, I have some ideas for a more efficient formula but this should get the job done. Please let me know how this works for you!
Cheers
[EDIT]
An alternative approach which allows the user to specify the number of previous entries to include is detailed below. This is a more general (preferred alternative) and picks up in place of the previously described step 3.
3Alt) In cell G2 enter a number of previous values to average, for this example I am sticking with 3. In cell E4 enter the following array expression (ctrl+shift+enter) and drag to the end of column E:
=IFERROR(IF(D4=1,SUM(INDEX(D:D,LARGE(($D$4:D4=1)*ROW($D$4:D4),$G$2)):D4 * INDEX(B:B,LARGE(($D$4:D4=1)*ROW($D$4:D4),$G$2)):B4)/$G$2,0),"")
This uses the LARGE function to find the 'nth' largest value, where n is the number of preceding values from the current time-step to average. Then it builds a range that extends from the found cell to the current time step. Then it multiplies the flags (0's and 1's) by each month's production value, sums them and divides by n. In this way months flagged as bad are set to 0 and not included in the sum.
This is a much cleaner way to achieve the desired result and has the flexibility to average different periods of time. See example of the final value below.