MJacobs1985

tsdat<-day%>%
  filter(location_identification==1)%>%
  mutate(egg_count = zoo::na.aggregate(egg_count))%>%
  dplyr::select(egg_count, date_interval)
tsdat<-xts(tsdat$egg_count, order.by=as.Date(tsdat$date_interval))
soliq_arimax = auto.arima(tsdat, 
                          seasonal= TRUE,
                          stepwise=FALSE,
                          approximation=FALSE, 
                          allowdrift = TRUE,

MJacobs1985

soliq_arimax = auto.arima(dfy[,"Eggs"], # specify main trend
                          xreg = dfexo[,c("TempMIN",
                                          "TempMAX",
                                          "TempACT", 
                                          "AnimalACT")], # specify exogenous variables here
                          trace = TRUE, 
                          seasonal= FALSE,
                          stepwise=FALSE,
                          approximation=FALSE)
summary(soliq_arimax);autoplot(soliq_arimax);checkresiduals(soliq_arimax)+theme_bw()

MJacobs1985

registerDoParallel(cores=8)
set.seed(123)
Eggs2<-day%>%
  filter(location_identification==1)%>%
  mutate(egg_count = zoo::na.aggregate(egg_count),
         feed_provided = zoo::na.aggregate(feed_provided),
         water_provided = zoo::na.aggregate(water_provided),
         animals_actual = zoo::na.aggregate(animals_actual),
         t_min_out = zoo::na.aggregate(t_min_out),
         t_max_out = zoo::na.aggregate(t_min_out),

MJacobs1985

m2 <- auto.arima(test, 
                 ic="aic", 
                 seasonal=FALSE, 
                 stationary=TRUE, 
                 stepwise=FALSE, 
                 approximation=FALSE)
ggtsdiag(m2, gof.lag = 14)+theme_bw()

m1.Mul.F <- forecast(m1, length(test))
plot(m1.Mul.F)

MJacobs1985

Eggsdf<-as.data.frame(Eggs)
far1 <- function(x, h){forecast(Arima(x, order=c(1,0,0), seasonal=FALSE), h=h)}
e <- tsCV(Eggsdf$egg_count, far1, h=1)
fc <- c(NA, Eggsdf$egg_count[2:149] - e[1:148])

train <-window(Eggs, end=2021.310)
autoplot(train)+theme_bw()
test  <-window(Eggs, start=2021.311)

m1 <- auto.arima(train, 

MJacobs1985

train <- window(Eggs,end=2021.310)
fit <- auto.arima(train)
refit <- Arima(Eggs, model=fit)
fc <- window(fitted(refit), start=2021.31)
# Multi-step, re-estimation
h <- 5
train <- window(Eggs,end=2021.310)
test <- window(Eggs,start=2021.311)
n <- length(test) - h + 1
fit <- auto.arima(train)

MJacobs1985

## External validation of VAR model ##
ext <- read_csv("ExternalVal.csv")
ext_var<-ext%>%
  filter(location_identification==1)%>%
  mutate(TempMIN = zoo::na.aggregate(t_min_out),
         TempMAX = zoo::na.aggregate(t_act_out),
         TempACT = zoo::na.aggregate(t_max_out), 
         AnimalACT = zoo::na.aggregate(animals_actual))%>%
  dplyr::select(TempMIN,TempMAX,TempACT,AnimalACT)%>%
  ts(.,

MJacobs1985

Serial1 <- serial.test(var.model_lev, 
                       lags.pt = 5, 
                       type = "PT.asymptotic");Serial1;plot(Serial1)

Arch1 <- arch.test(var.model_lev, 
                   lags.multi = 14, 
                   multivariate.only = TRUE);Arch1;plot(Arch1)

Norm1 <- normality.test(var.model_lev, 
                        multivariate.only = TRUE);Norm1

MJacobs1985

lagselect<-VARselect(dfy,
          lag.max = 14,
          type = "both", 
          season = 7);lagselect$selection

var.model_lev<-vars::VAR(dfy, 
                   p = 14, 
                   type = "both", 
                   season = 7, 
                   exogen=cbind(dfexo))

MJacobs1985

inds <- seq(as.Date(min(day$date_interval)), 
            as.Date(max(day$date_interval)), 
            by = "day")
Eggs<-day%>%
  filter(location_identification==1)%>%
  mutate(egg_count = zoo::na.aggregate(egg_count))%>%
  dplyr::select(egg_count)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)