Here, we have the following dataset containing a company’s sales records over different months. We will try to determine the autocorrelation for different lags in these records between these time ranges using the following 2 methods.

### Method 1: Using SUMPRODUCT, OFFSET, AVERAGE, and DEVSQ Functions to Calculate Autocorrelation

**Steps**:

- To calculate the total number of months to determine the time series of the sales values.

Enter the following function in cell**D13:**

`=COUNTA(B4:B12)`

**COUNTA** will determine the total months in the range **B4:B12**.

- Press
**ENTER**to get the total number of months:**9**.

- To determine the autocorrelations for the sales series between the lags
**1**to**5**.

Enter the following formula in cell**G4:**

`=SUMPRODUCT(OFFSET($D$4:$D$12,0,0,$D$13-F4)-AVERAGE($D$4:$D$12),OFFSET($D$4:$D$12,F4,0,$D$13-F4)-AVERAGE($D$4:$D$12))/DEVSQ($D$4:$D$12)`

Here, **$D$4:$D$12 **is the **Sales **range, **$D$13 **is the total number of months, and **F4 **is the lag value.

`$D$13-F4`

`becomes`

`9-1 â†’ 8`

`OFFSET($D$4:$D$12,0,0,$D$13-F4)`

`becomes`

`OFFSET($D$4:$D$12,0,0,8) â†’`

`extracts a range with a height of`

`8`

`rows from the reference cell`

`$D$4`

`.`

`Output â†’`

`{4996; 4137; 3203; 3403; 4831; 4931; 4753; 4381}`

`AVERAGE($D$4:$D$12) â†’`

`determines the average value of this range`

`Output â†’`

`4367.555`

`OFFSET($D$4:$D$12,0,0,$D$13-F4)-AVERAGE($D$4:$D$12)`

`becomes`

`{4996; 4137; 3203; 3403; 4831; 4931; 4753; 4381}-4367.555`

`Output â†’`

`{628.444; -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444}`

`OFFSET($D$4:$D$12,F4,0,$D$13-F4)`

`becomes`

`OFFSET($D$4:$D$12,1,0,8) â†’`

`the starting cell reference moves`

`1`

`cell downwards from`

`$D$4`

`and then extracts a range with a height of`

`8`

`rows from the reference cell`

`$D$5`

`Output â†’`

`{4137; 3203; 3403; 4831; 4931; 4753; 4381; 4673}`

`OFFSET($D$4:$D$12,F4,0,$D$13-F4)-AVERAGE($D$4:$D$12)`

`becomes`

`{4137; 3203; 3403; 4831; 4931; 4753; 4381; 4673}-4367.555`

`Output â†’`

`{ -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444; 305.444}`

`SUMPRODUCT(OFFSET($D$4:$D$12,0,0,$D$13-F4)-AVERAGE($D$4:$D$12),OFFSET($D$4:$D$12,F4,0,$D$13-F4)-AVERAGE($D$4:$D$12))`

`becomes`

`SUMPRODUCT({628.444; -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444},{ -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444; 305.444})`

`Output â†’`

`1287454.358`

`DEVSQ($D$4:$D$12) â†’`

`returns the sum of squares of the deviations of the data range from their mean.`

`Output â†’`

`3508950.222`

`SUMPRODUCT(OFFSET($D$4:$D$12,0,0,$D$13-F4)-AVERAGE($D$4:$D$12),OFFSET($D$4:$D$12,F4,0,$D$13-F4)-AVERAGE($D$4:$D$12))/DEVSQ($D$4:$D$12)`

`becomes`

`1287454.358/3508950.222`

`Output â†’`

`0.366905848`

- Press
**ENTER**and drag down the**Fill Handle**tool.

You will get the autocorrelations for the monthly sales series between a range of lags from **1 **to **5**.

**Read More: **How to Do Correlation and Regression Analysis in Excel

### Method 2 –Â Using SUMPRODUCT, AVERAGE, VAR.P Functions to Calculate Autocorrelation in Excel

**Steps**:

- Enter the following function in cell
**D13**to determine the total number of rows in the data series:

`=COUNTA(B4:B12)`

**COUNTA **will determine the total months in the range **B4:B12**.

- Press
**ENTER**to get the total number of months:**9**.

- To determine the autocorrelations for the sales series between the lags
**1**to**3**.

Enter the following formula in cell**G4:**

`=(SUMPRODUCT(D4:D11-AVERAGE(D4:D12),D5:D12-AVERAGE(D4:D12))/D13/VAR.P(D4:D12))`

Here, **D4:D11 **is the **Sales **range without the last cell value due to lag **1**, similarly, **D5:D12 **is the **Sales **range without the first cell value due to lag **1**, and **D13 **is the total number of months.

`AVERAGE(D4:D12) â†’`

`determines the average value of this range`

`Output â†’`

`4367.555`

`D4:D11-AVERAGE(D4:D12)`

`becomes`

`{4996; 4137; 3203; 3403; 4831; 4931; 4753; 4381}-4367.555`

`Output â†’`

`{628.444; -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444}`

`D5:D12-AVERAGE(D4:D12)`

`becomes`

`{4137; 3203; 3403; 4831; 4931; 4753; 4381; 4673}-4367.555`

`Output â†’`

`{ -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444; 305.444}`

`SUMPRODUCT(D4:D11-AVERAGE(D4:D12),D5:D12-AVERAGE(D4:D12))`

`becomes`

`SUMPRODUCT({628.444; -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444},{ -230.556; -1164.556; -964.556; 463.444; 563.444; 385.444; 13.444; 305.444})`

`Output â†’`

`1287454.358`

`SUMPRODUCT(D4:D11-AVERAGE(D4:D12),D5:D12-AVERAGE(D4:D12))/D13`

`becomes`

`1287454.358/9`

`Output â†’`

`143050.484224966`

`P(D4:D12) â†’`

`determines variance based on the entire range.`

`Output â†’`

`389883.358024691`

`(SUMPRODUCT(D4:D11-AVERAGE(D4:D12),D5:D12-AVERAGE(D4:D12))/D13/VAR.P(D4:D12))`

`becomes`

`(143050.484224966/389883.358024691)`

`Output â†’`

`0.366905848`

`Â`

- Press
**ENTER**.

You will get the autocorrelation of the sales series with their one-lagged version.

- To get the autocorrelation for lag=2, enter the following formula:

`=(SUMPRODUCT(D4:D10-AVERAGE(D4:D12),D6:D12-AVERAGE(D4:D12))/D13/VAR.P(D4:D12))`

**D4:D10 **represents the sales series without the last two values due to lag 2 and **D6:D12 **is the series without the first two values.

- You can have the autocorrelation for lag=3 by entering the following formula:

`=(SUMPRODUCT(D4:D9-AVERAGE(D4:D12),D7:D12-AVERAGE(D4:D12))/D13/VAR.P(D4:D12))`

**D4:D9 **represents the sales series without the last three values due to lag 3 and **D7:D12 **is the series without the first three values.

**Read More:** How to Calculate Cross Correlation in Excel

## Comparison of Results with Graphical Representation

We get the same autocorrelation values for their corresponding lags by using the above two formulas in two methods. Moreover, it can be concluded that with the increasing lagged values, we are experiencing a series of decreased autocorrelations.

For the lag value **1, **we have a positive value which represents a proportionate increase in this time interval. And, for the rest of the lag values, we have negative autocorrelations defining a proportionate decrease in the time intervals.

**Read More:** How to Make Correlation Graph in Excel

## Practice Section

**Download the Workbook**

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**<< Go Back toÂ Excel CorrelationÂ |Â Excel for Statistics****Â |Â Learn Excel**

Hi,

Interesting article. Thank you for sharing, but I am still confused about how to apply what you have shared in my application.

I have digitized vibration sensor data from rotating machinery. My set of values that I paste into the Excel column is 1808 cells long. This is the number of samples that are acquired in a single rotation of the shaft.

I am not an expert in Excel, or statistics. My understanding is that Autocorrelation should always result in values between +1 and -1. I would like to have an output that I can plot in a x-y line chart or better yet in a “chart” that represents one shaft rotation (for visualization purposes).

I do not know how many (lag) segments to use but once I understand how to properly configure this I could experiment with lag to find what might be appropriate.

Do you have a recommendation for how I should approach this?

Hello Allen,

Thank you for sharing your experience with us. I understand you want to know how the lag segments work to properly configure your digitized vibration sensor data from rotating machinery.

Here are factors to consider:

1. Longer data means you can use more lag values without losing reliability.

2. More frequent data collection might need more lag values to find detailed patterns.

3. If you expect specific patterns, use enough lag values to catch them.

4. To detect repeating patterns, use enough lags to cover potential periods.

5. More lags require more calculations, potentially slowing processing. Balance accuracy and efficiency based on available resources.

6. With your data resolution, you could start with around 40-50 lags to capture potential patterns within a rotation.

7. Finally, there’s no universally optimal lag length. It depends on your data, analysis objectives, and computational constraints.

Let’s calculate the autocorrelation with a data range similar to yours using a different technique. Since you have significantly large data, you can consider this method. I am going to calculate autocorrelation using Excel VBA to make the process faster and more accurate. Follow the steps:

1. Line up your data.

2. Run the below VBA code:

As a result, you will obtain the scatter chart visualizing your output.

You can use the effective methods mentioned in the article too. Hope this helps!

Regards,

Yousuf Khan Shovon