Use correlation to compare two time series? I have two time series data sets which contain hourly-intervalled, monthly, and yearly household electricity consumption in kWh. One data set is produced by a simulation, the other gathered from the real-world. My aim is to validate the simulated output by using the data gathered from the real-world.
I want to measure the similarity between these data sets, and be able to say if these are statistically similar. My first intuition is to use a correlation coefficient such a Pearson product moment correlation. But from what I read in previous posts that in general the correlation coefficient between two time-series may be a very poor metric.
I'm not very keen on statistics related to time series, but would something like a cross-correlation or maybe ARIMA do the trick? Could someone please point me in the direction for a technique which I can use in SPSS?
 A: Old thread but I'm adding this for others who may come across it. Cross correl is a fantastic tool for observing whether peaks in one series kind of 'lead' peaks in another series, so if you think there may be a predictor and response, but syncopated. For 24 observations it's fine, but your critical values (to be judged significant) are just higher, like with p-values for t-tests. Very useful but since the two datasets can't be a predictor and different variable response, maybe not in this case. 
For short time-series, arima needs 16 observations. It does show seasonality, dependance on adjacent observations, moving averages, etc. brilliant tool and spss has good tutorials. Minitab and SPSS pretty easy to use. Don't be put off by expertise, just give these things a try if you have time to read up, and they help you understand you data even if you don't use the eventual models. 
A: I have never used SPSS so I don't know if the Kolmogorov Smirnov test is implemented there. If it is, check it out. It will solve your problem
