Hi, I am trying to estimate an ARIMA model in the case where I have some specific knowledge about the coefficients that should be included in the model. Take a classical ARIMA (or even ARMA) model:
P(B) X(t) = Q(B) epsilon(t), where X(t) is the data, epsilon is a white noise, B is the backward operator and P and Q are some polynoms. Additionally, assume that you know in advance how P and Q look like. Typically, P could be something like this: P(x) = (1 - a(1)*x - a(2)*x^2) * (1 - b(1)*x^23 - b(2)*x^24) * (1 - c(1)*x^168) (That is in the case of hourly data, with lags 23 and 24 corresponding to the day, and lag 168 for the week.) How do you estimate this kind of model with R? The arima() and arima0() functions in the stats package do not allow this kind of constraints on the polynoms. I've searched in the packages dedicated to time series analysis, but I have not found a solution. Has anyone an idea? Thanks in advance! Laurent Duvernet EDF R&D ______________________________________________ R-help@stat.math.ethz.ch 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.