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daily time series data runs very slow on SSM #47
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Hard to say based on what you write, how many data points do you have and how long is the state vector? Is your model gaussian or non-gaussian? |
sorry I didn't give you the details earlier. The dataset has 1800 points. the model is gaussian and the model has 4 parameters: level, trend, seasonal, variance (H). Below is my model: model <-SSModel(data.1~SSMtrend(2,Q=list(NA,NA))+ ownupdatefn <- function(pars,model,...){ kfasfit <-fitSSM(inits=rep(log(var(data.1)),4), model=model, updatefn=ownupdatefn,method='BFGS') |
Ok, the issue is in your seasonal component, you have 250+ states which will slow down the recursions. You could try this kind of approach: https://robjhyndman.com/hyndsight/longseasonality/ (you can find the |
thank you for your response! I will try out your suggestions and check. |
Hello,
i have tried analyzing data of all frequencies in KFAS and daily data runs extremely slow and almost takes about 3 hours most of the time to stop running. i have used frequency of 365 and 250 to exclude all the holidays when analyzing stock price data and it is the same. is there a way to get results faster?
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