Finding correlations between financial time series I have a task which is related to finding correlations between time series. I have two financial time series given, which contain daily interest rate offers of two financial contributors and I want to find correlations (over time) between these quotes. The frequency and length is equal. The shape of the two time series is very similiar and looks like a upside down V. (upward trend followed by a downward trend)
My background in statistics, and particularly in time series analysis, is not very distinct. I know a few basics because I study mathematics but not more. I use the software R. My first approach was to calculate the Pearson correlation coefficient, but then I read a few topics about spurious correlation and fake correlations in trending time series so this could be not appropriate. Furthermore I read a few topics how to handle such problems, but I am still not sure how to solve my problem with my knowledge.
I would start as follows:
1) Use first differences or link relatives (which I found here: http://svds.com/avoiding-common-mistakes-with-time-series/) instead of absolute interest rates. 
2) The hope is to get weak-stationary series so that I can calculate correlation coefficients (Pearson/Spearman) and cross correlation for different lags. Am I getting this right? 
Would this course of action be appropriate to get meaningful results? How should I go on to solve the task? Im not looking for the perfect scientific solution, but I want to do a meaningful and solid analysis. Many thanks in advance!
 A: Find correlation between two time series. Theory and practice (R) discusses my road-map which is quite consistent with the very clear presentation that you cited  http://svds.com/avoiding-common-mistakes-with-time-series/ . The simple though profound idea is that to identify the intra-relationship one needs to adjust for any inter-relationship in the candidate X. I strongly suggest reading the seminal article on this subject https://www.jstor.org/stable/2341482?seq=1#page_scan_tab_contents . If you have problems obtaining it please feel free to contact me and I will try and help you.
Finally care must be taken to deal with possible anomalies as their untreated presence induces obfuscation yielding a downwards bias in statistical tests of significance much like a pebble affecting your glasses which will affect your vision.
I wonder about Google's econometricians/statisticians scholarship http://people.ischool.berkeley.edu/~hal/Papers/2015/primer.pdf as they continue to apply ordinary correlation tests (GOOGLE CORRELATE ; GOOGLE TRENDS) to time series data where angels fear to thread suggesting search processes based upon the naive assumption of no intra-correlation which is functionally equivalent to the assumption when analyzing cross-sectional data when trying to identify/short list significant predictors. 
A: To complete my comments, here is a way to display the scatterplot of the ranks:
library(MASS)

nsample = 500
mu <- c(0,0)

#high correlation: 0.9
correl = 0.8

sigma = matrix(c(1, correl, correl,1),2)
sample <- mvrnorm(nsample, mu = mu, Sigma = sigma )
x = sample[,1]
y = sample[,2]

rkx = rank(x)
rky = rank(y)

plot(rkx,rky)

#low correlation: 0.2
correl = 0.2

sigma = matrix(c(1, correl, correl,1),2)
sample <- mvrnorm(nsample, mu = mu, Sigma = sigma )
x = sample[,1]
y = sample[,2]

rkx = rank(x)
rky = rank(y)

plot(rkx,rky)

Higher the correlation, closer to the diagonal lie the points of your scatterplot. You will obtain the same whether using 1st diff or log-returns (invariance by increasing transformation of your variables).
What is Spearman correlation? It is just Pearson correlation applied to this scatterplot instead of the original data, that is the best linear fit to the ranks.
