Thanks for providing direction. By redefining time using as.yearmon(), I got acf() working.
Sent from my iPhone On Jan 14, 2025, at 6:51 PM, CALUM POLWART <polc1...@gmail.com> wrote: acf wants a time series, so tries to make one: as.ts(myts) Time Series: Start = 19357 End = 20027 Frequency = 1 [1] 24957 NA NA NA NA NA NA NA NA NA [11] NA NA NA NA NA NA NA NA NA NA [21] NA NA NA NA NA NA NA NA NA NA [31] NA 10577 NA NA NA NA NA NA NA NA [41] NA NA NA NA NA NA NA NA NA NA [51] NA NA NA NA NA NA NA NA NA NA [61] NA NA -18516 NA NA NA NA NA NA NA [71] NA NA NA NA NA NA NA NA NA NA [81] NA NA NA NA NA NA NA NA NA NA [91] 2940 NA NA NA NA NA NA NA NA NA [101] NA NA NA NA NA NA NA NA NA NA [111] NA NA NA NA NA NA NA NA NA NA [121] NA -1458 NA NA NA NA NA NA NA NA [131] NA NA I'm not familiar enough with TS to know how you make them 31d month units... On Tue, 14 Jan 2025, 23:08 Naresh Gurbuxani, <naresh_gurbux...@hotmail.com<mailto:naresh_gurbux...@hotmail.com>> wrote: For below data, I find strange results in basic time series analysis. Why does acf() function find missing values? When crossprod(xmat) is invertible, why does arima() find system exactly singular? Thanks, Naresh x <- c(24957, 10577, -18516, 2940, -1458, 32704, -26697, -46902, 48413, -11937, 2043, 26431, -55336, -16838, 89651, 25363, -50388, -41012, -28242, -18213, 58759, -15290, -7413, 124098) myts <- zoo::zoo(x, seq.Date(as.Date("2022-12-31"), as.Date("2024-11-30"), by = "1 month")) > myts 2022-12-31 2023-01-31 2023-03-03 2023-03-31 2023-05-01 2023-05-31 2023-07-01 24957 10577 -18516 2940 -1458 32704 -26697 2023-07-31 2023-08-31 2023-10-01 2023-10-31 2023-12-01 2023-12-31 2024-01-31 -46902 48413 -11937 2043 26431 -55336 -16838 2024-03-02 2024-03-31 2024-05-01 2024-05-31 2024-07-01 2024-07-31 2024-08-31 89651 25363 -50388 -41012 -28242 -18213 58759 2024-10-01 2024-10-31 -15290 -7413 > coredata(myts) |> class() [1] "numeric" > acf(myts, lag.max = 2) Error in na.fail.default(as.ts(x)) : missing values in object > arima(myts, order = c(1, 0, 0)) Error in solve.default(res$hessian * n.used, A) : Lapack routine dgesv: system is exactly singular: U[1,1] = 0 > xmat <- na.omit(cbind(x = myts, xlag1 = lag(myts, k = -1))) > crossprod(xmat) |> solve() x xlag1 x 3.488838e-11 1.949958e-12 xlag1 1.949958e-12 3.421268e-11 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org<mailto:R-help@r-project.org> mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
