Thanks Eric. Is it possible to know what R uses as the initial values? Are these configurable?
On Fri, 12 Aug 2022 at 13:44, Eric Berger <ericjber...@gmail.com> wrote: > The model that you are fitting to the data is an AR(2) model, which means > > y(t) = a0 + a1 * y(t-1) + a2 * y(t-2) + eps(t) (1) > > The fitting procedure estimates the coefficients a0, a1, a2 (and the > variance of eps). > > After the coefficients have been estimated, the fitted values can be > calculated using equation (1) (setting eps(t) = 0) > using > fitted(t) = a0 + a1 * y(t-1) + a2 * y(t-2) > > Assuming the series is given for t=1,2,...,20, there is no problem to > apply equation (1) to get the > fitted values for t=3,4,...,20. But there is a problem for t=1 and > t=2. For example, for t=1, equation (1) > says > > fitted(1) = a0 + a1 * y(0) + a2 * y(-1) > > But there are no values given for y(0) and y(-1). So, either no fitted > values should be given for t=1,2, or some > other method is being used. Apparently, the arima functions in R and > python use different methodology > to generate these two fitted points. (For all the other values, the > fits are extremely close.) > > HTH, > Eric > > On Thu, Aug 11, 2022 at 9:53 PM bogus christofer > <bogus.christo...@gmail.com> wrote: > > > > Hi, > > > > I have below AR model and fitted values from the forecast package > > > > library(forecast) > > dta = c(5.0, 11, 16, 23, 36, 58, 29, 20, 10, 8, 3, 0, 0, 2, 11, 27, 47, > 63, > > 60, 39) > > fit <- arima(dta, order=c(2,0,0)) > > fitted(fit) > > > > This gives fitted values as > > > > Time Series: > > Start = 1 > > End = 20 > > Frequency = 1 > > [1] 13.461017 9.073427 18.022166 20.689420 26.352282 38.165635 > 57.502926 > > 9.812106 15.335303 8.298995 11.543320 6.606999 5.800820 7.502621 > > 9.930962 19.723966 34.045298 49.252447 57.333846 44.615067 > > > > > > However when I compare this result with Python, I see significant > > difference particularly in the first few values as below > > > > from statsmodels.tsa.arima.model import ARIMA > > dta = [5.0, 11, 16, 23, 36, 58, 29, 20, 10, 8, 3, 0, 0, 2, 11, 27, 47, > 63, > > 60, 39] > > fit = ARIMA(dta, order=(2, 0, 0)).fit() > > fit.predict() > > > > array([21.24816788, 8.66048306, 18.02197059, 20.68931006, > > 26.35225759,38.16574655, 57.503278 , 9.81253693, 15.33539514, > > 8.29894655,11.54316056, 6.60679489, 5.80055038, 7.50232004, > > 9.93067155,19.72374025, 34.04524337, 49.25265365, 57.3343347 , > 44.6157026 ]) > > > > Any idea why there are such difference between R and Python results will > be > > very helpful. > > > > Thanks, > > > > [[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 > http://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 http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
