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Highlighted Post Prodigy

## Correlation between same column, different items for time periods

Afternoon,

I'm trying to find the correlation between the [close] column values of the 'StockbarDataExample' table for different companies in the 'StockSymbolExchangeCode' column.

I'm not sure how to compare the two time series given they share the same column for values.

1 ACCEPTED SOLUTION

Accepted Solutions
Highlighted Super User I

@ElliotP

In this sort of "pairwise comparison" scenario, where you have multiple entities in the same table (in this case distinguished by StockSymbolCurrencyI would normally follow the below steps.

My modifed copy of your pbix is here:
https://www.dropbox.com/s/y9h2ncitj46ee6a/PowerBiForumExample2%20Owen%20edit.pbix?dl=0

1. Create a copy of the entity dimension table, in your case a copy of ReferenceTable which I would call ReferenceTableComparison
2. Create a relationship between StockBarDatExample and ReferenceTableComparison, but make it inactive
3. Create the appropriate value measure that will be used for the company selected in ReferenceTable. For testing purposes I created
```Average Close =
AVERAGE ( StockBarDatExample[close] )```
4. Create the same measure for the Comparison Company, which activates the inactive relationship, and clears the filter on ReferenceTable:
```Average Close Comparison =
CALCULATE (
[Average Close],
ALL ( ReferenceTable ),
USERELATIONSHIP ( StockBarDatExample[StockSymbolCurrency], ReferenceTableComparison[StockSymbolCurrency] )
)```
5. You can then selected Company & Comparison Company using slicers, and use Average Close & Average Close Comparison together in visuals.
6. The Pearson Correlation Coefficient can be calculated using a method similar to that used here. This relies on having the above two measures set up.
Here is the measure I tested with your data:
```Pearson Correlation Coefficient =
VAR DateTimes =
// Create a table of date/times where both stocks have a Close value
FILTER (
SUMMARIZE ( StockBarDatExample, DateTable[DateKey], TimeTable[Column1] ), // Date & Time columns
AND (
NOT ( ISBLANK ( [Average Close] ) ),
NOT ( ISBLANK ( [Average Close Comparison] ) )
)
)
// Construct table of pairs of Close values
VAR Known =
SELECTCOLUMNS (
DateTimes,
"Known[X]", [Average Close],
"Known[Y]", [Average Close Comparison]
)
// Calculate correlation coefficient
VAR Count_Items =
COUNTROWS ( Known )
VAR Average_X =
AVERAGEX ( Known, Known[X] )
VAR Average_X2 =
AVERAGEX ( Known, Known[X] ^ 2 )
VAR Average_Y =
AVERAGEX ( Known, Known[Y] )
VAR Average_Y2 =
AVERAGEX ( Known, Known[Y] ^ 2 )
VAR Average_XY =
AVERAGEX ( Known, Known[X] * Known[Y] )
VAR CorrelationCoefficient =
DIVIDE (
Average_XY
- Average_X * Average_Y,
SQRT ( ( Average_X2 - Average_X ^ 2 ) * ( Average_Y2 - Average_Y ^ 2 ) )
)
RETURN
CorrelationCoefficient```

The test report page in the above pbix looks like this: Hopefully that helps. 🙂

Regards,

Owen

Owen Auger

5 REPLIES 5
Highlighted Community Support

HI @ElliotP,

You can use below formula to get diff between two legend:

```Diff =
VAR c1 =
FIRSTNONBLANK ( ALL ( ReferenceTable[CompanyName] ), [CompanyName] )
VAR c2 =
LASTNONBLANK ( ALL ( ReferenceTable[CompanyName] ), [CompanyName] )
RETURN
ABS (
CALCULATE (
SUM ( StockBarDatExample[close] ),
ReferenceTable[CompanyName] = c1
)
- CALCULATE (
SUM ( StockBarDatExample[close] ),
ReferenceTable[CompanyName] = c2
)
)
```

AFAIK, current line chart not support use multiple value field and legend at same time, I'd like to suggest you use 'line and clustered column chart' to instead. Regards,

Xiaoxin Sheng

Community Support Team _ Xiaoxin
If this post helps, please consider accept as solution to help other members find it more quickly.
Highlighted Post Prodigy

@v-shex-msftThanks for the response.

I'm not trying to calculate the difference between the different company's closes, but I think the principles might be able to work. I would like to be able to calculate the correlation between the time series data for each company; but I'm unable to at the moment as both time series values are in the same column ([close]), but they two time series are distinguishable by the 'StockSymbolCurrency' column string value.

The idea of using variables to seperate the time series values before returning a correlation figure is interesting, but I'm not sure how to get each variable to filter over subsequent different string values in the 'StockSymbolCurrency' column.

Highlighted Super User I

@ElliotP

In this sort of "pairwise comparison" scenario, where you have multiple entities in the same table (in this case distinguished by StockSymbolCurrencyI would normally follow the below steps.

My modifed copy of your pbix is here:
https://www.dropbox.com/s/y9h2ncitj46ee6a/PowerBiForumExample2%20Owen%20edit.pbix?dl=0

1. Create a copy of the entity dimension table, in your case a copy of ReferenceTable which I would call ReferenceTableComparison
2. Create a relationship between StockBarDatExample and ReferenceTableComparison, but make it inactive
3. Create the appropriate value measure that will be used for the company selected in ReferenceTable. For testing purposes I created
```Average Close =
AVERAGE ( StockBarDatExample[close] )```
4. Create the same measure for the Comparison Company, which activates the inactive relationship, and clears the filter on ReferenceTable:
```Average Close Comparison =
CALCULATE (
[Average Close],
ALL ( ReferenceTable ),
USERELATIONSHIP ( StockBarDatExample[StockSymbolCurrency], ReferenceTableComparison[StockSymbolCurrency] )
)```
5. You can then selected Company & Comparison Company using slicers, and use Average Close & Average Close Comparison together in visuals.
6. The Pearson Correlation Coefficient can be calculated using a method similar to that used here. This relies on having the above two measures set up.
Here is the measure I tested with your data:
```Pearson Correlation Coefficient =
VAR DateTimes =
// Create a table of date/times where both stocks have a Close value
FILTER (
SUMMARIZE ( StockBarDatExample, DateTable[DateKey], TimeTable[Column1] ), // Date & Time columns
AND (
NOT ( ISBLANK ( [Average Close] ) ),
NOT ( ISBLANK ( [Average Close Comparison] ) )
)
)
// Construct table of pairs of Close values
VAR Known =
SELECTCOLUMNS (
DateTimes,
"Known[X]", [Average Close],
"Known[Y]", [Average Close Comparison]
)
// Calculate correlation coefficient
VAR Count_Items =
COUNTROWS ( Known )
VAR Average_X =
AVERAGEX ( Known, Known[X] )
VAR Average_X2 =
AVERAGEX ( Known, Known[X] ^ 2 )
VAR Average_Y =
AVERAGEX ( Known, Known[Y] )
VAR Average_Y2 =
AVERAGEX ( Known, Known[Y] ^ 2 )
VAR Average_XY =
AVERAGEX ( Known, Known[X] * Known[Y] )
VAR CorrelationCoefficient =
DIVIDE (
Average_XY
- Average_X * Average_Y,
SQRT ( ( Average_X2 - Average_X ^ 2 ) * ( Average_Y2 - Average_Y ^ 2 ) )
)
RETURN
CorrelationCoefficient```

The test report page in the above pbix looks like this: Hopefully that helps. 🙂

Regards,

Owen

Owen Auger

Highlighted Post Prodigy

@OwenAugerThank you so much, that's amazing.

As an extension, from an idea, would it be possible to do this for a large number of comparisions? I get the feeling that might be better done with python and then exploring the resultant table and data from that instead of trying to use a tabular model to achieve that?

Highlighted Super User I

You're welcome 🙂

There's nothing to stop you having an arbitrary number of stocks in your source table...and you could use a matrix visual to show the correlation of every combination, or use that Correlation Plot custom visual. Or create your own R visual I guess

Perhaps performance might be better if you prepare the data with python (or R?) rather than computing on the fly - though don't have much experience with that myself.

Owen Auger  