# Overview

The Cross Correlation Processor computes the cross / auto correlation for given time series data.

The idea behind time shift is to compare a metric to another one with various "shifts in time". Applying a time shift to the normalized cross-correlation function will result in a "normalized cross-correlation with a time shift of X". This can be used to answer questions such as: "When many customers come in my shop, do my sales increase 20 minutes later?"

Further information about cross / auto correlation.

# Input

The cross correlation process is applied to a Dataset containing a timestamp column (minutes, hours, days..) along with at least one column with numeric values.

# Configuration

# Output

The output table contains the selected columns, the time delay applied and the corresponding auto / cross correlation values.

# Example

In this example we want to compute the auto / cross correlation between two numerical columns of a data set containing the occupancy rates for some accommodation services throughout the years 1999 to 2012.

## Input Example

## Workflow

## Example Configuration

Here we selected two numerical columns (HotelsOccupancyRate and MotelsOccupancyRate), the maximal time delay (time in column1 - time in column2) is 3, so the applied time delay will go from -3 to +3 months.

## Result

As we have selected two columns and a maximal delay value of 3 we have as a result 7*3 rows for respectively:

- The auto correlation of the hotels occupancy rate
- The cross correlation between the hotels occupancy rate and the motels occupancy rate
- The auto correlation of the motels occupancy rate