On 05-08-2013, at 10:32, Salaam Batur <swordligh...@gmail.com> wrote:
> Dear Berend, > > I am using R version 3.0.1 and I loaded code packages below > > library(TSA) > library(lattice) > library(stats) > library(utils) > library(forecast) > > I reisntalled R and loaded packages 'steries' and 'forecast' as you > suggested. Everything works out fine. Maybe it was the 'TSA' package which > gave me the warinig masseges > > Wonderful. Reinstalling R was most likely unnecessary. But please in future reply to the list only so that others can follow any discussion and also offer help. And show a working reproducible example. Berend > > > On Mon, Aug 5, 2013 at 3:03 PM, Berend Hasselman <b...@xs4all.nl> wrote: > > On 05-08-2013, at 07:47, Salaam Batur <swordligh...@gmail.com> wrote: > > > Dear R users, > > > > I am having a problem using forecast.Arima fuction. Here is the whole code: > > > > d=scan("D:/Data.txt") > > d > > D=ts(data=d, start=1981,end=2012, frequency=1) > > D > > Time Series: > > Start = 1981 > > End = 2012 > > Frequency = 1 > > [1] 384 403 427 450 499 550 575 615 640 680 702 730 760 790 > > [15] 790 830 870 871 906 920 968 1010 1060 1111 1165 1191 1217 1221 > > [29] 1089 1089 1090 1103 > > > > Please use dput to show objects. > > > lnD=log(D) > > lnD3=diff(lnD, differences=3) > > adf.test(lnD3) > > Augmented Dickey-Fuller Test > > > > data: lnD3 > > Dickey-Fuller = -3.5315, Lag order = 3, p-value = 0.05795 > > alternative hypothesis: stationary > > > > #########d parameter is determined by ADF test, which is 3############# > > > > Now choosing p and q parameters > > > > par(mfrow=c(2,1)) > > acf(lnD3, lag.max=20) > > pacf(lnD3, lag.max=20) > > > > ######### from acf and pacf, p=2, q=1############## > > > > Now fitting Arima > > > > arima1=arima(lnD3, order=c(2,0,1)) > > arima1 > > Series: x > > ARIMA(2,0,1) with non-zero mean > > > > Coefficients: > > ar1 ar2 ma1 intercept > > -0.5189 -0.2033 -1.0000 -1e-04 > > s.e. 0.1806 0.1770 0.0993 5e-04 > > > > sigma^2 estimated as 0.00118: log likelihood=54.24 > > AIC=-100.48 AICc=-97.87 BIC=-93.64 > > > > ####### Which looks good######### > > > > Using auto.arima() to see what R have in mind > > > > autoarima=auto.arima(lnD, d=3) > > autoarima > > Series: lnD > > ARIMA(2,3,0) > > > > Coefficients: > > ar1 ar2 > > -1.0282 -0.5851 > > s.e. 0.1524 0.1560 > > > > sigma^2 estimated as 0.001731: log likelihood=50.37 > > AIC=-94.73 AICc=-93.77 BIC=-90.63 > > > > ###### From AIC and BIC, I prefer arima1 instead of autoarima###### > > Now using forecast.Arima > > > > forecastArima1=foreca.Arima(arima1, h=5) > > *Warining message > > Error in ts(x) : 'ts' object must have one or more observations* > > > > I assume that this should be forecast.Arima > > > But forecasting autoarima is no problem > > forecastAutoArima=forecast.Arima(autoarima, h=5, c=(0.95)) > > forecastAutoArima > > > > Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 > > 2013 7.084688 7.031373 7.138004 7.003150 7.166227 > > 2014 7.167079 7.049206 7.284951 6.986808 7.347349 > > 2015 7.285478 7.069813 7.501142 6.955648 7.615308 > > 2016 7.443636 7.085484 7.801789 6.895890 7.991383 > > 2017 7.618670 7.081729 8.155612 6.797489 8.439852 > > > > Why???? Is there a bug probelm with arima() function itself??? > > > > This not reproducible code. > Which packages have you loaded? > adf.test, auto.arima and forecast.Arima are not standard. > > So using package sos told me that I needed package tseries and forecast. > > Using your data and your code gave me this for the code starting at > auto.arima: > > autoarima=auto.arima(lnD, d=3) > > autoarima > Series: lnD > ARIMA(2,3,0) > > Coefficients: > ar1 ar2 > -1.0282 -0.5851 > s.e. 0.1524 0.1560 > > sigma^2 estimated as 0.001731: log likelihood=50.37 > AIC=-94.73 AICc=-93.77 BIC=-90.63 > > > > forecastArima1=forecast.Arima(arima1, h=5) > > forecastArima1 > Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 > 2013 -0.0212777938 -0.06606177 0.02350618 -0.08976898 0.04721339 > 2014 0.0088824273 -0.07130043 0.08906529 -0.11374668 0.13151153 > 2015 -0.0004052589 -0.08451693 0.08370641 -0.12904295 0.12823243 > 2016 -0.0017177972 -0.08583008 0.08239448 -0.13035643 0.12692083 > 2017 0.0008517381 -0.08342997 0.08513345 -0.12804602 0.12974949 > > > > forecastAutoArima=forecast.Arima(autoarima, h=5, c=(0.95)) > > forecastAutoArima > Point Forecast Lo 80 Hi 80 Lo 95 Hi 95 > 2013 7.084688 7.031373 7.138004 7.003150 7.166227 > 2014 7.167079 7.049206 7.284951 6.986808 7.347349 > 2015 7.285478 7.069813 7.501142 6.955648 7.615308 > 2016 7.443636 7.085484 7.801789 6.895890 7.991383 > 2017 7.618670 7.081729 8.155612 6.797489 8.439852 > > So you must be doing something other than what you have shown. > > Berend > > > > If anyone knows the problem, or shows me a right direction, I would really > > appreciate it!!! > > Many many thanks!!! > > > > Chintemur Batur > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list > > 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. > > ______________________________________________ R-help@r-project.org mailing list 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.