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I have built a Kalman Filter model for flu forecasting as shown below. Y - Target Variable X1 - Predictor1 X2 - Predictor2 While forecasting into the future, I will NOT have data for all three variables. So, I am predicting X1 and X2 using two Kalman filters. The code is below x1.model <- dlmModSeas(52) + dlmModPoly(1, dV=5, dW=10) x2.model <- dlmModSeas(52) + dlmModPoly(1, dV=10, dW=10) x1.filter <- dlmFilter(c(train$x1, rep(NA, noofsteps)), x1.model) x2.filter <- dlmFilter(c(train$x2, rep(NA, noofsteps)), x2.model) Now, I am forecasting Y using the predicted X1 and X2 as bel...

...? This is the code: function (orig_ts){ library(dlm) dV_T <- 20000 dW_T <- c(100,10) m0_T <- rep(0,2) C0_T <- 10000*diag(nrow=2) my_dlmModPoly <- dlmModPoly(order=2, dV_T, dW_T, m0_T, C0_T) dV_S <- 20000 dW_S <- c(500,0,0) m0_S <- rep(0,3) C0_S <- 10000*diag(nrow=3) my_dlmModSeas <- dlmModSeas(frequency=4, dV_S, dW_S, m0_S, C0_S) my_mod <- my_dlmModPoly + my_dlmModSeas my_dlmFilter <- dlmFilter(orig_ts, my_mod) my_dlmBSample <- dlmBSample(my_dlmFilter[-1]) plot(orig_ts, type = 'o', ylim=c(-200, 550)) lines(my_dlmFilter$f[-1], col=2) lines(my_dlmFilte...