Timeline for How to read this ACF & PACF plots? [duplicate]
Current License: CC BY-SA 4.0
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| Jul 27, 2023 at 5:26 | history | edited | mkt |
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| Aug 10, 2018 at 11:55 | history | duplicates list edited | Glen_b | duplicates list edited from How to interpret these acf and pacf plots to Understanding the blue dotted lines in an ACF from R, Autocorrelation and partial autocorrelation interpretation, How to interpret these acf and pacf plots | |
| Aug 10, 2018 at 11:52 | history | closed | Glen_b r Users with the r badge or a synonym can single-handedly close r questions as duplicates and reopen them as needed. | Duplicate of How to interpret these acf and pacf plots | |
| Jul 26, 2018 at 20:50 | review | Close votes | |||
| Aug 3, 2018 at 14:49 | |||||
| Jul 12, 2018 at 23:57 | history | migrated | from stackoverflow.com (revisions) | ||
| Jul 12, 2018 at 19:14 | comment | added | Polime | @BenBolker when I use p=2 and then check for model improvement for p=3 or 4 through anova(model_p2,model_p3), it shows very significant p-value. What can I do now?? | |
| Jul 12, 2018 at 18:54 | comment | added | Ben Bolker | I would judge there's basically nothing going on here. The fact that PACF(2) (and ACF(4) is marginally beyond the 95% confidence interval is compensated by the fact that you're looking at about 25 comparisons ... | |
| Jul 12, 2018 at 18:24 | comment | added | Polime | i am using the following code: par(mfrow=c(1,2)) acf(residuals(model_ols), main="ACF") acf(residuals(model_ols), type = "partial", main="PACF")...There are 16 observations . I hope lag.max is fine. | |
| Jul 12, 2018 at 18:22 | history | asked | Polime | CC BY-SA 4.0 |