How to handle with multivariate time series?

[poiuytre]

Hello,

I have a pretty huge time series data. It is a matrix about 4000x2500 (4000 daily time series of sales gathered for 7years). After updating and changing file type to ts() I received a "large mts" data type.

It is a little bit problematic, because in whole functions which I want to use (connected for example with: cleaning the ts - tsclean(), tsoutliers(), calendar corrections - monthdays(), bizdays(), ets and arima forecasts and so on..) I get a errors like: "The time series is not univariate."

So, my question is: how to run such a functions in multivariate ts?

I've tried to use (in the example of usage of tsclean() and "mts data" is a data type in large mts):
sapply("mts data", FUN=tsclean("mts data")) or sapply("mts data"[1:2500, 1:4000], FUN=tsclean("mts data"))

and it also give a error:
"only univariate series are allowed" or "argument "x" is missing, with no default"

Do you have some directions how to handle with this problem?

[mara]

What packages are you using?

There's an extensive guide to multivariate time series analysis in R using the MARSS package here:

UserGuide.pdf

My go-to for forecasting is Robert J Hyndman's forecast:
https://robjhyndman.com/hyndsight/forecast7-part-2/

[poiuytre]

Thank you very much. I will try to use MARSS package. I didn't hear about it, but it seems to be a solution to my problems

One more time: thank you! You are great!