similar to: SARIMA with dynlm

Hi, I have a problem with the ARIMA function, occuring when I set the parameter per (the period of SARIMA model) to a high value (see the exemple bellow). It seems that when per is high it takes a too large amount of memory to calculate the model and I have a memory storage error. But I don't really understand why it takes more memory when per is high, as there is the same number of

Hi, I'm currently working with a SARIMA model from which I want to make simulations. As I understand, neither sarima.Sim nor the functions in the gsarima package use historic realizations of the time series to simulate future values. However, I want to use historic values as input and simulate future values based on the history. Anyone who know whether such a function is available

I want to set up a model with a formula and then run dynlm(formula) because I ultimately want to loop over a set of formulas (see end of post) R> form <- gas~price R> dynlm(form) Time series regression with "ts" data: Start = 1959(1), End = 1990(4) <snip> Works OK without a Lag term R> dynlm(gas ~ L(gas,1)) Time series regression with "ts" data: Start =

Hi, I was wondering why the results from lm and dynlm are not the same for what I think is the same model. I have just modified example 4.2 from the Pfaff book, please see below for the code and results. Can anyone tell my what I am doing wrongly? Many thanks, Werner set.seed(123456) e1 <- rnorm(100) e2 <- rnorm(100) y1 <- ts(cumsum(e1)) y2 <- ts(0.6*y1 + e2) lr.reg <- lm(y2

hi, I want to add different colors on the background of a classical plot. Each color is associated to an interval of the x axis. example: the background is red on the interval [1,10], blue on [11,20]. I try the rect function but it isn't appropriate for the background. Can any one can help me please? best regards. --------------------------------- [[alternative

I'm trying to fit a SARIMA(p,d,q)x(P,D,Q) with seasonal period s to some data. When dealing with these types of models one often looks at the ACF and PACF of the time series at lags that are multiples of s, to identify potential values of P, Q. How would I do this in R given the original time series? Secondly given a time series x acf(x) just gives me the plot of the acf. How would I actually

All: How do I obtain one step ahead out-of-sample forecasts from a model using "dyn" or "dynlm" ? Thanks! Best, John [[alternative HTML version deleted]]

Dear All, I'm trying to use dynlm to fit a time series. I have 3 seasonal terms. Here is an example of the problem. This is my time variable, hourly data: timeSeries <- seq(as.POSIXct("2011-01-01 00:00:00"), as.POSIXct("2011-12-31 23:00:00"), by="hour") My response is: y <- rnorm(length(t), 1000, 500) There are 3 seasonal factors: t.h <-

I would like to simulate some SARIMA models, e.g. a SARIMA (1,0,1)(1,0,1)[4] process. I installed the package 'forecast', where the function simulate.Arima should do what I am trying to do. I am not able to understand how it works Could somebody help me with an example? thank you Stefano Sofia AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni

i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S),

Hello, I would like to write a program that compute the principal components of a set of data and then 1. Run the dependent variable against the principal components (lagged value) 2. Do prediction , following Stock and Watson (1999) "Forecasting Inflation". All data are time series. Now I can run the program using single factor (first principal component), but I

Each of the data sets contains monthly observations on price indices for 7 countries. I use the fitted values from reg1 in the reg2 model. The interior loop executes without error as long as I explicitly specify the data set, i.e. data=dat70. However the code fails to execute if I specify the model in the form of the commented line, i. e reg1 <-dynlm(form1,data=Dnames[j]) I get the following

Dear useRs, I've release a new version of the "dynlm" package to CRAN which adds two new features: o instrumental variables regression (two-stage least squares) via formulas like dynlm(y ~ x1 + x2 | z1 + z2 + z3, data = mydata) where z1, z2, z3 are the instruments which can again contain lags/differences/season via the d()/L()/season() operators. o

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Hi, My query is related to ARIMA function in stats package. While looking for the time series literature I found following link which highlights discrepancy in "arima" function while dealing with differenced time series. Is there a substitute function similar to "sarima" mentioned in the following website implemened in R? Any pointers would be of great help.

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Suppose we have the time series “y” When I regress this series on its first lag then I use the following command in dynlm as r=dynlm( y~L(y,1)) if I put the command of summary of above regression model then by the command summary (r ) I will get the following out put elements Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) __

Dear useRs, recently, there were several discussions on R-help about how to conveniently fit dynamic linear models and time series regressions. The package dynlm tries to address this problem by 1. providing some more functions like lags L() and differences d() and season() in the formula specification of a model and 2. preserving the time series attributes of the data. A first version of the

Dear useRs, recently, there were several discussions on R-help about how to conveniently fit dynamic linear models and time series regressions. The package dynlm tries to address this problem by 1. providing some more functions like lags L() and differences d() and season() in the formula specification of a model and 2. preserving the time series attributes of the data. A first version of the

Hi..   R Program is shown ARIMA output as below then SARIMA equation is be   (1 - 0.991B^{12})z_t + 43.557 = (1+0.37B)(1-0,915B^{12})a_t    But I try to calculate it by manual . It look like it 's big different from R sofeware,   I am not sure this equation is correct or not . PLS supoort me to confirm it   Arima Model ( 0,0,1)(1,0,1)   No Transformation Constant   >> 43.557 , t = 10.09