What is a time series comparison?
A cross-section looks at a single point in time, which is useful for comparing and analyzing the effect of different factors on one another or describing a sample. Time series involves repeated sampling of the same data over time.
What statistical test should I use to compare 4 groups?
Therefore, methods typically used for within-participant comparisons (e.g. paired/dependent samples t-tests, repeated measures ANOVA, etc.) would normally be appropriate.
How to statistically compare two time series?
This common model could be estimated globally and separately for each of the two series and then one could construct an F test to test the hypothesis of a common set of parameters. Consider the grangertest () in the lmtest library. It is a test to see if one time series is useful in forecasting another. Just came across this.
What’s the difference between t test and time series?
With t-tests, you are comparing the mean of each group and you are assuming that the groups consist of independent observations with equal variances (the latter is sometimes relaxed). When testing time series, the assumption of independence is usually not reasonable, but then you need to replace it with a specified correlation structure…
Is there a good correlation between two series?
If the r is small your conclusion would be that they are weakly related and so no desirable comparisons and a larger value if r would suggest good comparisons s between the two series. The third step where there is good correlation is to test the statistical significance of the r.
Which is the best model to describe each series separately?
With that in place I would identify a common model that would reasonably describe each series separately. This might be an ARIMA model or a multiply-trended Regression Model with possible Level Shifts or a composite model integrating both memory (ARIMA) and dummy variables.