gistfile1.txt

mymts<-dataset%>%dplyr::select(VO2i_methode1,DO2i_methode1,RQ_methode1)%>%ts(.)
mymts
plot(mymts)
theme_set(theme_bw())
autoplot(mymts) +
  ggtitle("Time Series Plot of the `mymts' Time-Series") +
  theme(plot.title = element_text(hjust = 0.5)) #for centering the text
class(mymts)
apply(mymts, 2, adf.test)
plot.ts(mymts)
autoplot(ts(mymts))
VARselect(mymts, 
          type = "none", #type of deterministic regressors to include. We use none becasue the time series was made stationary using differencing above. 
          lag.max = 10)
var.a <- vars::VAR(mymts,
                   lag.max = 10, #highest lag order for lag length selection according to the choosen ic
                   ic = "AIC", #information criterion
                   type = "none") #type of deterministic regressors to include
summary(var.a)
serial.test(var.a)
plot(var.a)
causality(var.a, #VAR model
          cause = c("VO2i_methode1"))
fcast = predict(var.a, n.ahead = 500) # we forecast over a short horizon because beyond short horizon prediction becomes unreliable or uniform
par(mar = c(2.5,2.5,2.5,2.5))
plot(fcast)