k41m4n / ssm Goto Github PK

Dear,

I want to fit a Multivariate Local Linear Trend model to my data. My dataset consist of 4 time series but I really don't know how to fit a Multivariate Local Linear Trend model to my data. I already defined something but it is completely wrong. Can you please help me?

covid_new <- ts(data = covid_new, frequency = 1, start = c(1))
ts.plot(window(covid_new[, 1:4]), col = 1:4,
ylab = "Log confirmed cases",
xlab = "Time")
legend("topright",col = 1:4, lty = 1, legend = colnames(covid_new)[1:4])

covid_new <- window(covid_new, start = 1, end = 145)
model <- SSModel(covid_new[, 1:4] ~ SSMtrend(2, Q = list(matrix(NA, 4, 4), matrix(NA, 4, 4))), H = matrix(NA, 4, 4))

updatefn <- function(pars, model, ...) {
diag(H) <- exp(pars[1:4])
Q <- diag(exp(pars[1:4]))
Q[upper.tri(Q)] <- pars[5:10]
model["Q", etas = "level"] <- crossprod(Q)
Q <- diag(exp(pars[11:14]))
Q[upper.tri(Q)] <- pars[15:20]
model["Q", etas = "slope"] <- crossprod(Q)
model
}

init <- chol(cov(covid_new[, 1:4]))
fitinit <- fitSSM(model, updatefn = updatefn,
inits = rep(c(diag(init), init[upper.tri(init)], init[upper.tri(init)]), 2),
method = "BFGS")
-fitinit$optim.out$val

fit <- fitSSM(model, updatefn = updatefn, inits = fitinit$optim.out$par, method = "BFGS", nsim = 250)
-fit$optim.out$val

varcor <- fit$model["Q", etas = "level"]
varcor[upper.tri(varcor)] <- cov2cor(varcor)[upper.tri(varcor)]
print(varcor, digits = 2)

varcor <- fit$model["Q", etas = "slope"]
varcor[upper.tri(varcor)] <- cov2cor(varcor)[upper.tri(varcor)]
print(varcor, digits = 2)

(out <- KFS(fit$model, nsim = 1000))

Dear,

My dataset consists 4 time series. I already fitted a Local Linear Trend on this data but now I want to add a regression to this model. The only problem is that I want to regress index_new[, 1] on covid_new[, 1], index_new[, 2] on covid_new[, 2] and so on. Is that possible using SSMregression?