Calculate the Pearson Correlation value by comparing data to another sensor. A value of 1 indicate highly correlated data.

The Pearson Correlation measures the linear correlation between two variables, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation.



Input Data*

Defines the time series data fed into the function. This can be a sensor ID or another function.


Number of data intervals considered in the function

Sensor ID

Enter Sensor Identification for correlation

*Input data is optional in most cases. If Info360 detects that the first input is time series data, it will be applied to the function. Otherwise, the current active sensor's data will be used, which is often the case in Reference Charts.

Example Usage as an Expression:

Correlation(Sensor('Sensor_A'),12,'Sensor_B') - Outputs the correlation coefficient between Sensor_A and Sensor_B over the previous 12 data points.

Examples Reference Chart:

The following chart displays the correlation between measured tank levels and modeled results from an InfoWater model. In this case, InfoWater results are pushed to an Updatable Sensor so they can be reviewed and shared in Info360.

The model correlates well with the measurements well except for where the model peaks late compared to measurements. Here the correlation drops to -1 because over a 6 period interval one stream is rising while the other is draining which gives a strong negative correlation.


For information on setting up custom equations and syntax, please refer to Analytical Functions.