Correlating volume timeseries Consider the following graph:

The red line (left axis) describes the trading volume of a certain stock. The blue line (right axis) describes the twitter message volume for that stock. For instance, on May 9 (05-09) about 1.100 million trades and 4.000 tweets were made.
I would like to calculate whether there is a correlation between the timeseries, either on the same day or with a lag - for instance: tweet volume correlates with trading volume one day later. I'm reading many articles who have made such analysis, for instance Correlating Financial Time Series with Micro-Blogging Activity, but they do not describe how such an analysis is made in practical terms. The following is stated in the article:

However, I have very little experience with statistical analysis and don't know how to execute this on the series that I have. I use SPSS (also known as PASW) and my question is: what are the steps to take to make such an analysis from the point where I have a datafile underlying the above image? Is such a test a default feature (and what is it called) and/or how could I else execute it?
Any help would be greatly appreciated :-)
 A: Two check for bivariate normality check three things:


*

*check if first series of observations is marginally normal,

*check if second series of observations is marginally normal,

*regress on one the other  and check if residuals are normal.


To check normality at each of these steps, use normal q-q plots or you can use any normality hypothesis test.
Or alternatively you could check if every possible linear combination (real coefficients) of the two series is marginally normal. That would probably be difficult, though.
Edit: (6 years later)
I'll keep the above for posterity, but note I have a more recent answer to a similar question here.
A: The correlation coefficient between time series is useless. See CORRELATION COEFFICIENT - Critical values for Testing Significance. This was first pointed out by U. Yule in 1926 Yule, G.U, 1926, "Why do we sometimes get nonsense correlations between time series? A study in sampling and the nature of time series", Journal of the Royal Statistical Society 89, 1–64. You might want to google "why do we get nonsense correlation" for more. 
The reason for this is tests for correlation requite joint normality. Joint normality requires each series to be normal. Normality requires independence. To examine the relationship between time series please review Transfer Function Identification in any good time series book like Time Series Analysis: Univariate and Multivariate Methods, by William W.S. Wei, David P. Reilly.
Challenge Answer
In terms of an answer to your challenge. It is well known, by a few (Yule, G.U, 1926) that correlating two time series can be flawed particularly if either series is affected by pulses/level shifts/seasonal pulses and/or local time trends. That being the case I would take each of the series SEPARATELY and identify the ARIMA structure and any pulses/level shifts/seasonal pulses and/or local time trends that might apply and create an error process. 
With two clean error processes, one for each of the two original series, I would compute the cross correlation which could then be used to measure the degree of association above and beyond the auto-correlative structure within each series. This solution is appropriately called the double pre-whitening approach. 
See:


*

*Model Identification in Dynamic Regression (Distributed Lag) Models

*Analysis of fMRI Timeseries: Linear Time-Invariant Models, Event-related fMRI and Optimal Experimental Design or

*Time Series Analysis: Univariate and Multivariate Methods, by William W.S. Wei, David P. Reilly
