I have estimated a univariate time series model, consisting of a random walk and an AR component. Now the goal is to make forecast about a couple of steps ahead as new data comes in, in an online fashion. so in R, using DLM package:
filter <- dlmFilter(training_data, model) forecast <- dlmForecast(filter, 2)
Now the problem is that it seems every time a new data point comes in, I have to reconstruct the filter and then do the forecast, which is very slow. Also, I think, one of the promises of using DLM is that it does not need to store the whole dataset. Is there a way to update the filter as new data comes in without going through all the data?