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Errors can be both positive or negative. So if you just add them up (without squaring them), then the positive terms will cancel out the negative terms. This means that you are actually ignoring the e …

If you want to use regression here, then check all the assumptions of the model carefully after fitting. The errors should be uncorrelated for example. Your $Y$ here is time dependent and it is very l …

You didn't provide your data. So I am guessing what you are looking for: I think you want to get something like this: > library(forecast) > fit=Arima(WWWusage,c(3,1,0)) > AA11a<-forecast(fit) > AA11a …

You don’t need to write a forecast function. It has been already written in the “forecast” package. First save the fitted object, then use forecast function. Here is an example: > library(forecast) > …

On the other hand, if you look at the book Introduction to Time Series and Forecasting (2nd Ed.) by Peter J. Brockwell, Richard A. Davis, P. 161, the maximum range for both p and q is from 0 to 27. …

This enables me to simulate the $X_t$ over the forecasting period and gives a sequence of zeros and ones. Since this is a rare event, I will not see $X_t=1$ often. … Question: How can I develop an efficient simulation procedure to take into account the occurrence of 1’s in the simulated $X_t$ over the forecasting period? …

I want to perform a simulation study to obtain the mean of the forecasted values and the 95% forecasting intervals and compare them with the “Exact” mean and 95 forecasting intervals. …

There is NO such a thing as "most efficient methods for forecasting in R". You as a forecaster need to figure it out which model is good for the question you are answering. …

Check this link Box-Jenkins modelling Another reference is page 169, Introduction to Time Series and Forecasting, Brockwell and Davis, “Once the data have been transformed (e.g., by some combination of …

I am trying to forecast with an arimax model including the xtransf argument. I used the example given in chapter 11 of the book Time Series Analysis With Applications in R, Jonathan D. Cryer & Kung-Si …

As for your 2nd question: yes indeed ARIMA is a forecasting model. …

No, they are not comparable. Because you are fitting your ets to two different data sets, one with and the other one without BoxCox transformation. By default, when you don't provide the argument of l …

I have a linear regression model with some correlated errors: $Y_t=\beta_0+\beta_1X_1+\beta_2X_2+\epsilon_t$, where $\epsilon_t$ is a AR(1) i.e. $\epsilon_t=\phi\epsilon_{t-1}+\nu_t$ with $\nu_t$ as w …

Q1: Is there any Arima (p,d,q) model that can forecast interventions (pulse) itself? I know that I can use xreg or even xtransf arguments as the covariates to include the intervention over the observe …

Change your mean to something smaller than 400 like 0.01 to see the time series more clearly. Then try the following code with rho <- 0.1 and rho <- 0.9 to see the effect of rho. rho <- 0.1 #rho <- 0 …