To (hopefully) clarify the question a bit more if you have a broad working hypothesis that x influences y, on a causal/predictive basis (e.g. if x goes up in time t, then you would expect y to go up in time t+1, assuming the working hypothesis is true).
In traditional time-series analysis the length of time that the period t represents for t and t+1 tends to be equal. Can alterations in the periods that each represents, be easily altered for the purposes of analysis?
Also if we take the example of t to be a day, is it possible to distinguish differences in predictivity for various time frames, e.g. x goes up today, and over the course of the next 3 days y goes up, or over the course of the next 5 days y goes down (what happens if they are both significant?).
How does one go about detecting the time-frame of prediction appart from brute-forcing every possible combination of before and after timeframes (e.g. testing if x has gone up over the course of the last m days what happens to y over the next 1 to n days).