I have time series data of experimental observations for two related processes. I want to measure correlation for use in further analysis.
Correlation of the series changes over time and across different length sliding windows on the data. To clarify, I might want to look at correlation over 10, 20, 30, 40, ..., n periods, each of these essentially sliding windows across the data. Kind of analogous to looking at a bunch of simple moving average windows.
Historically and over future observations, one of these correlation windows will prove a better representation of the data than the rest. But the random nature of the underlying processes (whose distribution one may not know) makes settling on one window by evaluating the data historically an unsound approach.
Universal - A possible approach?
Information theory applied to the areas of data compression and portfolio allocation has produced what often gets referred to as a “Universal” approach to attacking a similar problem.
Thomas Cover a principle advocate for the idea saw the universal approach as a general method for multi-variate optimization of random processes even where one had no idea of the underlying distribution.
Cover's book, Elements of Information Theory, looks at a couple of examples of this idea. Cover seeing this as an optimization technique led me to ask this question here on the Mathematics SE site rather that say the statistics site.
Example - Universal data compression
In Universal Data Compression, one can see that an internet router can’t know the optimal data compression algorithm to use prior to its actually reading the packet of information or stream of data. Universal Data Compression addresses the problem with the following steps:
Ranks each of its available compression algorithms as to their effectiveness after each stream of data has passed through the router.
Calculates the cumulative ranking for each of the compression algorithms
Identifies the mean cumulative rank weighted compression algorithm.
Uses this mean cumulative rank weighted algorithm on the next stream of data it receives.
Note that the approach does not use a follow the leader strategy.
Over time this method will asymptopically approach the effectiveness of the best single algorithm that one could have picked at the beginning. A Darwin quote captures the key idea behind a Universal approach:
"It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change."
Universal approaches keep one in a pretty good place most of the time, which does very well over time.
So it occurred to me that I might develop a related method, Universal Correlation
with the following steps:
Rank each correlation window’s effectiveness (more about this below) at each time step.
Calculate the cumulative ranking for each of the correlation windows
Identify the mean cumulative rank weighted correlation window (or the one nearest the mean).
I'll then use this in another stage of analysis.
Now my question
What measure can I use to rank each correlation window’s effectiveness at each time step?
The following earlier question may have some bearing on an answer:
This may help clarify: At a given point in time, I have calculated correlation for some number of windows on the data up until that point in time (going back, 10 periods, 20 periods, 30, periods, ...).
Now I need a measure to rank the effectiveness or accuracy of each of the time periods correlation relative to each other.
Put yet another way: What do I rank the measures of correlation against?