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Timeline for Understanding this acf output

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Jan 3, 2019 at 16:17 comment added whuber @ayhan Yes, that makes sense and is a method frequently used when it is known or suspected that the process will not be quite stationary but its deviations from stationarity are locally small and when the analysis (such as forecasting, interpolation, or whatever) also is going to be a local one.
Jan 3, 2019 at 15:43 comment added user21227 @whuber I was referring to the means of the series you called prefix and suffix. If I have 50 observations and want to calculate the autocorrelation at lag 5 that would give me the first 45 observations as the prefix and the last 45 observations as the suffix. They would have different means. In the Wikipedia autocorrelation article the difference between Eq. 2 and 4 is basically what I was asking. Would it be correct to calculate autocorrelation like in Eq 2?
Jan 3, 2019 at 15:21 comment added whuber @ayhan Could you explain what "their corresponding means" refers to?
Jan 3, 2019 at 11:28 comment added user21227 Regarding the second factor: Is it because we assume a stationary series that we use the mean of the entire series? Does it become incorrect if we use their corresponding means (and calculate correlation)?
Jan 10, 2014 at 23:45 vote accept zsljulius
Jan 10, 2014 at 22:14 comment added whuber I inserted a new section to answer that question, zsljulius. It concludes that the ACF approximates the correlation coefficient between the beginning and end of the series, as lagged by $k$, but that it tends to be a little smaller in absolute value and in your example grows more negative, too, because the series starts off with less-than-average values and ends up with greater-than-average values.
Jan 10, 2014 at 22:09 history edited whuber CC BY-SA 3.0
added 4725 characters in body
Jan 10, 2014 at 0:15 comment added zsljulius Thanks for your answer. Intuitively, what does acf measure exactly?
Jan 9, 2014 at 21:51 history answered whuber CC BY-SA 3.0