I wonder if there is a test for comparing means of two different time series (both measured at the same person or two different persons)? Would be very glad if someone can help me!
17557
I have two time series, shown in the plot below:    The plot is showing the full detail of both time series, but I can easily reduce it to just the coincident observations if needed.  My question is: What statistical methods can I use to assess the differences between the time series?  I know this is a fairly broad and vague question, but I can't seem to find much introductory material on this anywhere. As I can see it, there are two distinct things to assess:  1. Are the values the same?  2. Are the trends the same?  What sort of statistical tests would you suggest looking at to assess these questions? For question 1 I can obviously assess the means of the different datasets and look for significant differences in distributions, but is there a way of doing this that takes into account the time-series nature of the data?  For question 2 - is there something like the Mann-Kendall tests that looks for the similarity between two trends? I could do the Mann-Kendall test for both datasets and compare, but I don't know if that is a valid way to do things, or whether there is a better way?  I'm doing all of this in R, so if tests you suggest have a R package then please let me know.
9024
I have a question related to modeling short time-series. It is not a question , but how. What method would you recommend for modeling (very) short time-series (say of length $T \leq 20$)? By "best" I mean here the most robust one, that is the least prone to errors due the fact of limited numbers of observations. With short series single observations could influence the forecast, so the method should provide a cautious estimate of errors and possible variability connected to the forecast. I am generally interested in univariate time-series but it would be also interesting to know about other methods.
47837
I have an existing data set that comes from the same group of people before and after they received a treatment.  The data set comes from when participants tested their blood sugar values over a 30 day period before receiving an insulin pump and a 30 day period after receiving an insulin pump. This data was obtained from user logs (archival data) and was not controlled to ensure that they were tested at fixed intervals. Participants tested themselves when they needed to test throughout the day for a period of 30 days.  My goal is to determine whether the average blood sugar across 30 days was different for the before vs after group.   Normally this would be a paired samples t-test but unfortunately the before and after group have an unequal number of data points with the after group having significantly more. People are testing more often after receiving an insulin pump.  What is the correct way to handle this?  I can collapse the data to find a mean for each participant before treatment and after treatment (across all participants) so that the data matches up and then run a paired samples t-test on this data but I think this is not the ideal solution.   Would a one way within subjects ANOVA be the appropriate test to run in this case?
91469
Suppose I have following non-periodic time series. Obviously the trend is decreasing and I would like to prove it by some test (with p-value). I am unable to use classic linear regression due to strong temporal (serial) auto-correlation among values.  library(forecast) my.ts &lt;- ts(c(10,11,11.5,10,10.1,9,11,10,8,9,9,                6,5,5,4,3,3,2,1,2,4,4,2,1,1,0.5,1),             start = 1, end = 27,frequency = 1) plot(my.ts, col = "black", type = "p",      pch = 20, cex = 1.2, ylim = c(0,13)) # line of moving averages  lines(ma(my.ts,3),col="red", lty = 2, lwd = 2)     What are my options?
16999
Hi I am trying to find the non-parametric equivalent of a two-way ANOVA (3x4 design) which is capable of including interactions. From my reading in Zar 1984 "Biostatistical analysis" this is possible using a method put forth in Scheirer, Ray, and Hare (1976), however, according to other posts online it was inferred that this method is no longer appropriate (if it ever was).  Does anyone know what method would be appropriate for doing so, and if so the corresponding functions in R or Stata?
23095
I have a data set from a customer survey, I want to deploy a statistical test to see whether there is significance difference between product 1 and product 2.  Here is a data set of customers' reviews.   The rate is from very bad,bad,okay,good,to very good.  customer    product1    product2 1           very good   very bad 2           good        bad 3           okay        bad 4           very good   okay 5           bad         very good 6           okay        good 7           bad         okay 8           very good   very bad 9           good        good 10          good        very good 11          okay        okay 12          very good   good 13          good        good 14          very good   okay 15          very good   okay   What methods should I use to see if there is any difference betw these two products?
101014
I did experiment on one group of rats, the group was tested during 8 days, this is the first stage. The same group was also tested again but this time the test phase was 7 days. I tried to do my analysis using ANOVA repeated measures but I had a problem with the days.. I want to know the difference in the 2 tests but the days are not the same number. I don't know how to handle it. If there is any way to do the analysis using repeated measures ANOVA.   I tried to use repeated measure anova within subjects effect. My first factor will be test with 2 levels (test 1 &amp; 2),second factor is day with the level of ?? Here is the problem! if I write 8 there will be extra day in test 2 and the analysis cant carry!!
84118
I have following problem:   Within an independent groups 1-factor design I have two independent groups, with a sample size of 20 each. The data of the treatment group is not normally distributed, whereas the data for the control group is (checked with Shapiro-Wilk Normality Test). Now I want to check if the differences of the means of both groups are significant.  What is the appropriate test for this? I think it should be the Wilcoxon Rank Sum and Signed Rank Test, but I am not sure...  Could please anybody help me?
23111
I understand that a stationary time series is one whose mean and variance is constant over time. Can someone please explain why we have to make sure our data set is stationary before we can run different ARIMA or ARM models on it? Does this also apply to normal regression models where autocorrelation and/or time is not a factor?
118940
I have an experiment with two groups under different conditions where a single dependent variable is measured repeatedly at multiple times.  It seems the right parametric test to use here is :     "A mixed ANOVA compares the mean differences between groups that have   been split on two "factors" (also known as independent variables),   where one factor is a "within-subjects" factor and the other factor is   a "between-subjects" factor."   However, my data is strongly non-normal (and some of the dependent variables are ordinal anyway).    What is a non-parametric equivalent test to two-factor mixed ANOVA?
43570
I'm a little confused regarding the intraclass correlation coefficient and one-way ANOVA.  As I understand it, both tell you how similar observations within a group are, relative to observations in other groups.  Could someone explain this a little better, and perhaps explain the situation(s) in which each method is more advantageous?
89556
Can someone explain to me why would anyone choose a parametric over a nonparametric statistical method for hypothesis testing or regression analysis?   In my mind, it's like going for rafting and choosing a non-water resistant watch, because you may not get it wet. Why not use the tool that works on every occasion?
85672
I have a question about cluster analysis. There are 3000 companies, which have to be clustered according to their power usage over 5 years. Each company has values for every hour during 5 years. I would like to find out if some companies have the same pattern in usage power over the time period. The results should be used for daily prediction of power usage. If you have some ideas how to cluster time series in SPSS, please share with me.
39340
It seems that a number of the statistical packages that I use wrap these two concepts together. However, I'm wondering if there are different assumptions or data 'formalities' that must be true to use one over the other. A real example would be incredibly useful.
113125
I have 2 time-series (both smooth) that I would like to cross-correlate to see how correlated they are.  I intend to use the Pearson correlation coefficient. Is this appropriate?  My second question is that I can choose to sample the 2 time-series as well as I like. i.e. I can choose how many data points I will us. Will this affect the correlation coefficient that is output? Do I need to account for this?  For illustration purposes  option(i)  [1,    4,    7,    10] &amp; [6,    9,    6,    9,    6]  option(ii)  [1,2,3,4,5,6,7,8,9,10] &amp; [6,7,8,9,8,7,6,7,8,9,8,7,6]
54125
seems to be a well known and renowned machine learning expert in the field of finance. I am very far from his level, as have not yet finished my PhD in economics, and only have an applied level statistical knowledge. I have encountered a much . I can not say I completely understand all the mathematical parts. But there are some claims in the paper, which seem to completely contradict things, which I have learned on statistics and economics thus far, or at least seem to be illogical for me. For a specific example, the paper under the section Pitfall #4 and Solution #4 suggests, that by differencing time series to make them stationary for classical statistical models (ARIMA etc.) removes the memory of the series and thus makes them lose the predictive power:     The conclusion is that, for decades, most empirical studies have   worked with series where memory has been unnecessarily wiped-out. The   reason this is a dangerous practice is that fitting a memory-less   series will likely lead to a spurious pattern, a false discovery.   Incidentally, this over-differentiation of time series may explain why   the Efficient Markets Hypothesis is still so prevalent among academic   circles: Without memory, series will not be predictive, and   researchers may draw the false conclusion that markets are   unpredictable.   In economics there really is a simplified theoretical model on equity returns, which posits, that it is a memory-less white noise, and the prices (the integrated returns) follow a random walk. But from an empirical perspective, as far as I understand the memory-less attribute of returns pertains only to the individual data points themselves, not the series as a whole. A differentiated series still should have a "collective" memory put together, and it has almost the same information as the integrated version, only lacks a constant value. So it should have the same predictive power as well, should it not? Or it is me, who has a lack of understanding?
39036
"An event less frequent in the long run is likely to happen before a more frequent event!"  How can I show that THTH is more likely to turn up before HTHH with a probability of  9/14, even though waiting time of THTH is 20 and HTHH, 18!  I would be very thankful if you could show me the way of  calculating the probability of turning up earlier,   and the waiting time. Thank you!