Correlation analysis is the process that indicates the similarity and relationship between two random variables. For time series data, correlation analysis would be done between two sets of the datasets. And in non time-series data, correlation analysis would generally be done between two independent variables in the dataset. So, in this recipe, we would look at the correlation analysis of time series (signals).
You have to have the StatsBase package ready. This can be done by running:
using StatsBase
Autocovariance is the covariance of a piece of time data with itself at two different time points. This helps in understanding how correlated the time series is, with respect to the time dimension. This metric helps us find trends across the time dimension of a time series across different time points. It can be calculated with default lags using the
autocov()function:autocov(x)The output would look like the following:
Now, if we want to set...
