Cross correlation interpretation

Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs.

1. How do you interpret cross-correlation results?
2. What does negative cross-correlation mean?
3. What does Corr value mean?
4. What does a negative CCF mean?

How do you interpret cross-correlation results?

If the slope is positive, the cross correlation is positive; if there is a negative slope, the cross correlation is negative. This helps to identify important lags (or leads) in the process and is useful for application when there are predictors in an ARIMA model.

What does negative cross-correlation mean?

A negative correlation describes the extent to which two variables move in opposite directions. For example, for two variables, X and Y, an increase in X is associated with a decrease in Y. A negative correlation coefficient is also referred to as an inverse correlation.

What does Corr value mean?

The correlation coefficient is a statistical measure of the strength of a linear relationship between two variables. Its values can range from -1 to 1. A correlation coefficient of -1 describes a perfect negative, or inverse, correlation, with values in one series rising as those in the other decline, and vice versa.

What does a negative CCF mean?

In R, the sample CCF is defined as the set of sample correlations between x t + h and for h = 0, ±1, ±2, ±3, and so on. A negative value for is a correlation between the x-variable at a time before and the y-variable at time . For instance, consider = −2. The CCF value would give the correlation between x t − 2 and .