I'm thinking that 'convolution' is a mathematical process or manipulation done on numbers, or a sequence of numbers etc, while correlation is about determining similarity or likeness between maybe a set of values (when compared with a given reference set of values). The results of a convolution process is maybe ONE tool (or mathematical manipulation process) for seeing if there is any degree of relation or similarity (aka correlation) between data sets. So, if you 'convolve' one particular set of values from a 'reference' time sequence with some other similar sequence, the plotted result could give you a visual indication of similarity between the data sets. And if a person isn't around to look at the results, then a computer program could search through the result to see if there are relatively large enough 'peaks' in the results of the convolution ---- so can get a computer to do the assessing of the results. And, then there are cases involving lots of measurements on 1 quantity to get an average value (to present to somebody). And lots of measurements on a different (other) quantity. In that case, there are matrix correlation methods involving these other statistical values like covariance, mean etc, which involve a correlation method, but not using any convolution method.