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app.R
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app.R
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library(dplyr)
library(shiny)
library(fabletools)
library(tsibbletalk)
library(ggplot2)
library(plotly)
library(patchwork)
library(forecast)
library(fable)
library(fabletools)
library(gt)
library(shinythemes)
library(shinyWidgets)
library(fresh)
library(gt)
library(data.table)
ui <- fluidPage(
titlePanel(h1("Forecast Dengue Incidence in Sri Lanka", align = "center")),
theme = shinytheme("cyborg"),
sidebarPanel(
column(12, dateRangeInput('dateRange',
label = 'Filter Historical Data by Date',
start = "2006-12-29",
end = "2020-12-18",
min = "2006-12-29",
max = "2020-12-18"),),
tags$hr(),
radioButtons("structure", h5("Hierarchical structure:"),
list( "Spatial Hierarchy" = "SH",
"Temporal hierarchy" = "TH"
)),
br(),
radioButtons("Method", h5("Select the forecasting method:"),
list( "Base forecasting" = "BF",
"Hierarchical forecasting" = "HF"
)),
br(),
selectInput("period", h5("Select forecast period:"),
choices = c("1 year", "2 years", "3 years", "4 years", "5 years")),
br(),
downloadButton("downloadData", "Download"),
h6("Click download button and wait until forecasts are generated.")
),
mainPanel(
tabsetPanel(
tabPanel("Description",
helpText(h6("This dengue incidence forecasting app provides an overview of the dengue epidemic
in Sri Lanka from existing data. This app is built with R software.")),
tags$h4("Code"),
helpText(h6("The code of this app is available on, ",
tags$a(href="https://github.com/SamudraMadushani/DengueSriLankaApp.git",
"Github"),)),
helpText(h4("Data")),
helpText(h6("Data were obtained from Epidemiological Unit, Ministry of Health, Sri Lanka")),
helpText(h4("Outputs")),
helpText(h6("Firstly, this app provides the time series plots
corresponding to series associated with the spatial or
temporal hierarchical structure of Sri Lanka.
You can obtain the individual series forecasts from
ETS, NAIVE, SNAIVE, and average approaches.
These approaches are called base forecasts. Furthermore,
you can obtain the hierarchical forecasts based on
spatial or temporal hierarchical structure in order to
obtain aligned decisions for the given forecast period
and selected historical data are used for the modeling process."))),
tabPanel("Data",
column(8,
dataTableOutput('my_table')
)),
tabPanel("Visualization",plotlyOutput("summary1")),
tabPanel("Forecasts",
column(8,
gt_output(outputId = "table")))),
)
)
server <- function(input, output, session) {
DengueTEST <- reactive({
df <- read.csv("Data.csv")
Date<-df$EndDate
Province<-df$Province
Districts<-df$Districts
Counts<-as.integer(df$Dengue)
df<-data.frame(Date,Counts,Districts,Province)
return(df)
})
output$my_table <- renderDataTable({
# Filter the data
DengueTEST<-DengueTEST() %>% filter(Date >= input$dateRange[1] & Date <= input$dateRange[2])
DengueTEST
})
output$summary1 <- renderPlotly({
if(input$structure=="SH"){
DengueTEST<-DengueTEST() %>% filter(Date >= input$dateRange[1] & Date <= input$dateRange[2])
DengueTEST$Week<-tsibble::yearweek(DengueTEST$Date)
DataDengueTEST<- DengueTEST%>%
as_tsibble(key=c(Province,Districts),index=Week)
ProvinceDistricts_shared <- DataDengueTEST%>%
as_shared_tsibble(spec = (Province/Districts))
p01<- plotly_key_tree(ProvinceDistricts_shared, height = 700, width = 600)
p1n <- ProvinceDistricts_shared %>%
ggplot(aes(x = Date, y = Counts)) +
geom_line(aes(group = Districts), alpha = 0.5,color='darkblue')
DIS_DataDengueTEST<-DataDengueTEST %>%fabletools::aggregate_key(Province/Districts,Counts=sum(Counts)) #ARRANGE TEST SET
SL<-DIS_DataDengueTEST%>%
dplyr::filter(is_aggregated(Province) & is_aggregated(Districts)) %>%
autoplot(Counts,col="#e7298a") + labs(y = "Counts",x="Week")
DIS_AGG_SRS<-DIS_DataDengueTEST %>%
dplyr::filter(is_aggregated(Districts)& !is_aggregated(Province)) %>%
autoplot(Counts) +
labs(y = "Counts",x="Week") +
theme(legend.position = "none")
subplot(p01,
subplot(
ggplotly(p1n, tooltip = "Districts", width = 900),
ggplotly(DIS_AGG_SRS, width = 900),
ggplotly(SL, width = 900),
#p2nN,
nrows = 3),
widths = c(.5, .5)) %>%
highlight(dynamic = TRUE)
}
else{
DengueTEST<-DengueTEST() %>% filter(Date >= input$dateRange[1] & Date <= input$dateRange[2])
DengueTEST$Week<-tsibble::yearweek(DengueTEST$Date)
DataDengueTEST<- DengueTEST%>%
as_tsibble(key=c(Province,Districts),index=Week)
Weekly_Dengue<-DataDengueTEST%>%
tsibble::index_by(WD=~yearweek(.))%>%
dplyr::summarise(Counts=sum(Counts))
#Weekly_Dengue<-Weekly_Dengue%>%dplyr::filter(WD!=53)
Dengue_total_data<-c(Weekly_Dengue$Counts)
Dengue_total_data<- ts(Dengue_total_data, freq=52, start=c(lubridate::year(DengueTEST$Date[1]),lubridate::week(DengueTEST$Date[1])))
aggts_total = thief::tsaggregates(Dengue_total_data)
W<-autoplot(aggts_total[[1]],col="#e7298a")
BW<-autoplot(aggts_total[[2]],col="#1b9e77")
M<-autoplot(aggts_total[[3]],col="#d95f02")
Q<-autoplot(aggts_total[[4]],col="#7570b3")
SA<-autoplot(aggts_total[[5]],col="#66a61e")
A<-autoplot(aggts_total[[6]],col="#66a61e")
#TEM<-(W/BW/M/Q/Y)
#TEM<-(W|BW|M)/(Q|Y)
a <- list(text = "Weekly",showarrow = FALSE)
b <- list(text = "Bi-weekly",showarrow = FALSE)
c <- list(text = "Monthly",showarrow = FALSE)
d <- list(text = "Quarterly",showarrow = FALSE)
e <- list(text = "Semi-annual",showarrow = FALSE)
f <- list(text = "Annual",showarrow = FALSE)
subplot(ggplotly(W, width = 900,height = 600)%>%layout(annotations = a),
ggplotly(BW, width = 900,height = 600)%>%layout(annotations = b),
ggplotly(M, width = 900,height = 600)%>%layout(annotations = c),
ggplotly(Q, width = 900,height = 600)%>%layout(annotations = d),
ggplotly(SA, width = 900,height = 600)%>%layout(annotations = e),
ggplotly(A, width = 900,height = 600)%>%layout(annotations = f),
nrows = 3)
}
})
datasetInput <- reactive({
switch(
"1 year" = 1,
"2 years" = 2,
"3 years" = 3,
"4 years" = 4,
"5 years" = 5)
})
Fvalues <- reactive({
if(input$structure=="SH"){
DengueTEST<-DengueTEST() %>% filter(Date >= input$dateRange[1] & Date <= input$dateRange[2])
DengueTEST$Week<-tsibble::yearweek(DengueTEST$Date)
DataDengueTEST<- DengueTEST%>%
as_tsibble(key=c(Province,Districts),index=Week)
if(input$Method=="BF"){
fit_TRAIN <- DataDengueTEST%>%
model(snaive=SNAIVE(Counts),naive=NAIVE(Counts),ets=ETS(Counts),avg = MEAN(Counts))
Fvalues<-fit_TRAIN %>% forecast(h=input$period)
Fvalues <- rename(Fvalues, "PointForecast"=".mean")
Fvalues <- rename(Fvalues, "Model"=".model")
Fvalues<-data.frame(Fvalues)
Fvalues$PointForecast<-round(Fvalues$PointForecast,0)
Fvalues<-Fvalues[,c(1,2,3,4,6)]
}
else{
fit_TRAIN <- DataDengueTEST%>%
model(snaive=SNAIVE(Counts),naive=NAIVE(Counts),ets=ETS(Counts),avg = MEAN(Counts))%>%
fabletools::reconcile(
bu_ets = bottom_up(ets),
#top_ets=top_down(ets),
ols_ets=min_trace(ets,method="ols"),
#MinT_ets=min_trace(ets,method="mint_shrink"),
wls_var_ets = min_trace(ets,method="wls_var"),
wls_struct_ets=min_trace(ets,method="wls_struct")
#MinT_cov_ets=min_trace(ets,method="mint_cov")
)
# Forecast value calculation
Fvalues<-fit_TRAIN %>% forecast(h=input$period)
Fvalues <- rename(Fvalues, "PointForecast"=".mean")
Fvalues <- rename(Fvalues, "Model"=".model")
Fvalues<-data.frame(Fvalues)
Fvalues$PointForecast<-round(Fvalues$PointForecast,0)
Fvalues<-Fvalues[,c(1,2,3,4,6)]
}
}
else{
DengueTEST<-DengueTEST() %>% filter(Date >= input$dateRange[1] & Date <= input$dateRange[2])
DengueTEST$Week<-tsibble::yearweek(DengueTEST$Date)
DataDengueTEST<- DengueTEST%>%
as_tsibble(key=c(Province,Districts),index=Week)
Weekly_Dengue<-DataDengueTEST%>%
tsibble::index_by(WD=~yearweek(.))%>%
dplyr::summarise(Counts=sum(Counts))
#Weekly_Dengue<-Weekly_Dengue%>%dplyr::filter(WD!=53)
Dengue_total_data<-c(Weekly_Dengue$Counts)
Dengue_total_data<- ts(Dengue_total_data, freq=52, start=c(lubridate::year(DengueTEST$Date[1]),lubridate::week(DengueTEST$Date[1])))
aggts_total = thief::tsaggregates(Dengue_total_data)
base <- list()
if(input$Method=="BF"){
BASE_TemporalHierachicalForecasting<-function(Dengue_total_data,fcastsFUN)
{
aggts_total = thief::tsaggregates(Dengue_total_data)
base1 <- list()
base2 <- list()
base3 <- list()
base4 <- list()
base5 <- list()
base6 <- list()
if(input$period=="1 year"){
base1<- forecast(fcastsFUN(aggts_total[[1]]), h=1*frequency(aggts_total[[1]]))
base2<- forecast(fcastsFUN(aggts_total[[2]]), h=1*frequency(aggts_total[[2]]))
base3<- forecast(fcastsFUN(aggts_total[[3]]), h=1*frequency(aggts_total[[3]]))
base4<- forecast(fcastsFUN(aggts_total[[4]]), h=1*frequency(aggts_total[[4]]))
base5<- forecast(fcastsFUN(aggts_total[[5]]), h=1*frequency(aggts_total[[5]]))
base6<- forecast(fcastsFUN(aggts_total[[6]]), h=1*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:length(m1)){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name2 <- c("BiWeek_1")
for(i in 2:length(m2)){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name3 <- c("Month_1")
for(i in 2:length(m3)){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name4 <- c("Quarter_1")
for(i in 2:length(m4)){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name5 <- c("SemiAnnual_1")
for(i in 2:length(m5)){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name6<-"Annual_1"
}
else if(input$period=="2 years"){
base1<- forecast(fcastsFUN(aggts_total[[1]]), h=2*frequency(aggts_total[[1]]))
base2<- forecast(fcastsFUN(aggts_total[[2]]), h=2*frequency(aggts_total[[2]]))
base3<- forecast(fcastsFUN(aggts_total[[3]]), h=2*frequency(aggts_total[[3]]))
base4<- forecast(fcastsFUN(aggts_total[[4]]), h=2*frequency(aggts_total[[4]]))
base5<- forecast(fcastsFUN(aggts_total[[5]]), h=2*frequency(aggts_total[[5]]))
base6<- forecast(fcastsFUN(aggts_total[[6]]), h=2*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,2)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,2)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,2)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,2)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,2)
name6<-"Annual_1"
name6<-rep(name6,2)
}
else if(input$period=="3 years"){
base1<- forecast(fcastsFUN(aggts_total[[1]]), h=3*frequency(aggts_total[[1]]))
base2<- forecast(fcastsFUN(aggts_total[[2]]), h=3*frequency(aggts_total[[2]]))
base3<- forecast(fcastsFUN(aggts_total[[3]]), h=3*frequency(aggts_total[[3]]))
base4<- forecast(fcastsFUN(aggts_total[[4]]), h=3*frequency(aggts_total[[4]]))
base5<- forecast(fcastsFUN(aggts_total[[5]]), h=3*frequency(aggts_total[[5]]))
base6<- forecast(fcastsFUN(aggts_total[[6]]), h=3*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,3)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,3)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,3)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,3)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,3)
name6<-"Annual_1"
name6<-rep(name6,3)
}
else if(input$period=="4 years"){
base1<- forecast(fcastsFUN(aggts_total[[1]]), h=4*frequency(aggts_total[[1]]))
base2<- forecast(fcastsFUN(aggts_total[[2]]), h=4*frequency(aggts_total[[2]]))
base3<- forecast(fcastsFUN(aggts_total[[3]]), h=4*frequency(aggts_total[[3]]))
base4<- forecast(fcastsFUN(aggts_total[[4]]), h=4*frequency(aggts_total[[4]]))
base5<- forecast(fcastsFUN(aggts_total[[5]]), h=4*frequency(aggts_total[[5]]))
base6<- forecast(fcastsFUN(aggts_total[[6]]), h=4*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,4)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,4)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,4)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,4)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,4)
name6<-"Annual_1"
name6<-rep(name6,4)
}
else if(input$period=="5 years"){
base1<- forecast(fcastsFUN(aggts_total[[1]]), h=5*frequency(aggts_total[[1]]))
base2<- forecast(fcastsFUN(aggts_total[[2]]), h=5*frequency(aggts_total[[2]]))
base3<- forecast(fcastsFUN(aggts_total[[3]]), h=5*frequency(aggts_total[[3]]))
base4<- forecast(fcastsFUN(aggts_total[[4]]), h=5*frequency(aggts_total[[4]]))
base5<- forecast(fcastsFUN(aggts_total[[5]]), h=5*frequency(aggts_total[[5]]))
base6<- forecast(fcastsFUN(aggts_total[[6]]), h=5*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,5)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,5)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,5)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,5)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,5)
name6<-"Annual_1"
name6<-rep(name6,5)
}
#Weekly results
l1<-as.double(round(base1$lower[,1],2))
u1<-as.double(round(base1$upper[,1],2))
mth1<-c(rep(base1$method,length(m1)))
# Bi-Weekly results
l2<-as.double(round(base2$lower[,1],2))
u2<-as.double(round(base2$upper[,1],2))
mth2<-c(rep(base1$method,length(m2)))
# Monthly results
l3<-as.double(round(base3$lower[,1],2))
u3<-as.double(round(base3$upper[,1],2))
mth3<-c(rep(base1$method,length(m3)))
#Quarterly results
l4<-as.double(round(base4$lower[,1],2))
u4<-as.double(round(base4$upper[,1],2))
mth4<-c(rep(base1$method,length(m4)))
# SemiAnnual results
l5<-as.double(round(base5$lower[,1],2))
u5<-as.double(round(base5$upper[,1],2))
mth5<-c(rep(base1$method,length(m5)))
# Annual results
l6<-as.double(round(base6$lower[,1],2))
u6<-as.double(round(base6$upper[,1],2))
mth6<-c(rep(base1$method,length(m6)))
# data set
nameA<-c(name1,name2,name3,name4,name5,name6)
m<-c(m1,m2,m3,m4,m5,m6)
l<-c(l1,l2,l3,l4,l5,l6)
u<-c(u1,u2,u3,u4,u5,u6)
mth<-c(mth1,mth2,mth3,mth4,mth5,mth6)
thf<-data.frame(nameA,mth,m,l,u)
colnames(thf)<-c("Time","Method","Point forecast","Lo 80","Hi 80")
return(thf)
}
AAetsB<-BASE_TemporalHierachicalForecasting(Dengue_total_data,ets)
BASE_TemporalHierachicalForecastingNOmodel<-function(Dengue_total_data,fcastsFUN)
{
aggts_total = thief::tsaggregates(Dengue_total_data)
base1 <- list()
base2 <- list()
base3 <- list()
base4 <- list()
base5 <- list()
base6 <- list()
if(input$period=="1 year"){
base1 <- fcastsFUN(aggts_total[[1]], h=1*frequency(aggts_total[[1]]))
base2 <- fcastsFUN(aggts_total[[2]], h=1*frequency(aggts_total[[2]]))
base3 <- fcastsFUN(aggts_total[[3]], h=1*frequency(aggts_total[[3]]))
base4 <- fcastsFUN(aggts_total[[4]], h=1*frequency(aggts_total[[4]]))
base5 <- fcastsFUN(aggts_total[[5]], h=1*frequency(aggts_total[[5]]))
base6 <- fcastsFUN(aggts_total[[6]], h=1*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:length(m1)){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name2 <- c("BiWeek_1")
for(i in 2:length(m2)){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name3 <- c("Month_1")
for(i in 2:length(m3)){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name4 <- c("Quarter_1")
for(i in 2:length(m4)){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name5 <- c("SemiAnnual_1")
for(i in 2:length(m5)){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name6<-"Annual_1"
}
else if(input$period=="2 years"){
base1 <- fcastsFUN(aggts_total[[1]], h=2*frequency(aggts_total[[1]]))
base2 <- fcastsFUN(aggts_total[[2]], h=2*frequency(aggts_total[[2]]))
base3 <- fcastsFUN(aggts_total[[3]], h=2*frequency(aggts_total[[3]]))
base4 <- fcastsFUN(aggts_total[[4]], h=2*frequency(aggts_total[[4]]))
base5 <- fcastsFUN(aggts_total[[5]], h=2*frequency(aggts_total[[5]]))
base6 <- fcastsFUN(aggts_total[[6]], h=2*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,2)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,2)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,2)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,2)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,2)
name6<-"Annual_1"
name6<-rep(name6,2)
}
else if(input$period=="3 years"){
base1 <- fcastsFUN(aggts_total[[1]], h=3*frequency(aggts_total[[1]]))
base2 <- fcastsFUN(aggts_total[[2]], h=3*frequency(aggts_total[[2]]))
base3 <- fcastsFUN(aggts_total[[3]], h=3*frequency(aggts_total[[3]]))
base4 <- fcastsFUN(aggts_total[[4]], h=3*frequency(aggts_total[[4]]))
base5 <- fcastsFUN(aggts_total[[5]], h=3*frequency(aggts_total[[5]]))
base6 <- fcastsFUN(aggts_total[[6]], h=3*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,3)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,3)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,3)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,3)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,3)
name6<-"Annual_1"
name6<-rep(name6,3)
}
else if(input$period=="4 years"){
base1 <- fcastsFUN(aggts_total[[1]], h=4*frequency(aggts_total[[1]]))
base2 <- fcastsFUN(aggts_total[[2]], h=4*frequency(aggts_total[[2]]))
base3 <- fcastsFUN(aggts_total[[3]], h=4*frequency(aggts_total[[3]]))
base4 <- fcastsFUN(aggts_total[[4]], h=4*frequency(aggts_total[[4]]))
base5 <- fcastsFUN(aggts_total[[5]], h=4*frequency(aggts_total[[5]]))
base6 <- fcastsFUN(aggts_total[[6]], h=4*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,4)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,4)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,4)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,4)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,4)
name6<-"Annual_1"
name6<-rep(name6,4)
}
else if(input$period=="5 years"){
base1 <- fcastsFUN(aggts_total[[1]], h=5*frequency(aggts_total[[1]]))
base2 <- fcastsFUN(aggts_total[[2]], h=5*frequency(aggts_total[[2]]))
base3 <- fcastsFUN(aggts_total[[3]], h=5*frequency(aggts_total[[3]]))
base4 <- fcastsFUN(aggts_total[[4]], h=5*frequency(aggts_total[[4]]))
base5 <- fcastsFUN(aggts_total[[5]], h=5*frequency(aggts_total[[5]]))
base6 <- fcastsFUN(aggts_total[[6]], h=5*frequency(aggts_total[[6]]))
m1<-round(base1$mean,0)
m2<-round(base2$mean,0)
m3<-round(base3$mean,0)
m4<-round(base4$mean,0)
m5<-round(base5$mean,0)
m6<-round(base6$mean,0)
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,5)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,5)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,5)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,5)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,5)
name6<-"Annual_1"
name6<-rep(name6,5)
}
#Weekly results
l1<-as.double(round(base1$lower[,1],2))
u1<-as.double(round(base1$upper[,1],2))
mth1<-c(rep(base1$method,length(m1)))
# Bi-Weekly results
l2<-as.double(round(base2$lower[,1],2))
u2<-as.double(round(base2$upper[,1],2))
mth2<-c(rep(base1$method,length(m2)))
# Monthly results
l3<-as.double(round(base3$lower[,1],2))
u3<-as.double(round(base3$upper[,1],2))
mth3<-c(rep(base1$method,length(m3)))
#Quarterly results
l4<-as.double(round(base4$lower[,1],2))
u4<-as.double(round(base4$upper[,1],2))
mth4<-c(rep(base1$method,length(m4)))
# SemiAnnual results
l5<-as.double(round(base5$lower[,1],2))
u5<-as.double(round(base5$upper[,1],2))
mth5<-c(rep(base1$method,length(m5)))
# Annual results
l6<-as.double(round(base6$lower[,1],2))
u6<-as.double(round(base6$upper[,1],2))
mth6<-c(rep(base1$method,length(m6)))
# data set
nameA<-c(name1,name2,name3,name4,name5,name6)
m<-c(m1,m2,m3,m4,m5,m6)
l<-c(l1,l2,l3,l4,l5,l6)
u<-c(u1,u2,u3,u4,u5,u6)
mth<-c(mth1,mth2,mth3,mth4,mth5,mth6)
thf<-data.frame(nameA,mth,m,l,u)
colnames(thf)<-c("Time","Method","Point forecast","Lo 80","Hi 80")
return(thf)
}
AAnaiveB<-BASE_TemporalHierachicalForecastingNOmodel(Dengue_total_data,forecast::naive)
AAsnaiveB<-BASE_TemporalHierachicalForecastingNOmodel(Dengue_total_data,forecast::snaive)
AAavgB<-BASE_TemporalHierachicalForecastingNOmodel(Dengue_total_data,forecast::meanf)
Fvalues<-data.frame(AAnaiveB,AAsnaiveB,AAavgB,AAetsB)
Fvalues = data.table(Fvalues)
}
else{
TemporalHierachicalForecasting<-function(Dengue_total_data,fcastsFUN)
{
aggts_total = thief::tsaggregates(Dengue_total_data)
base <- list()
for(i in seq_along(aggts_total)){
if(input$period=="1 year"){
base[[i]] <- forecast(fcastsFUN(aggts_total[[i]]), h=1*frequency(aggts_total[[i]]))
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name6<-"Annual_1"
}
else if(input$period=="2 years"){
base[[i]] <- forecast(fcastsFUN(aggts_total[[i]]), h=2*frequency(aggts_total[[i]]))
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,2)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,2)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,2)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,2)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,2)
name6<-"Annual_1"
name6<-rep(name6,2)
}
else if(input$period=="3 years"){
base[[i]] <- forecast(fcastsFUN(aggts_total[[i]]), h=3*frequency(aggts_total[[i]]))
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,3)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,3)
name3 <- c("Month_1")
for(i in 2:13){
names13<-paste("Month_",i)
name3<-c(name3,names13)
}
name3<-rep(name3,3)
name4 <- c("Quarter_1")
for(i in 2:4){
names14<-paste("Quarter_",i)
name4<-c(name4,names14)
}
name4<-rep(name4,3)
name5 <- c("SemiAnnual_1")
for(i in 2){
names15<-paste("SemiAnnual_",i)
name5<-c(name5,names15)
}
name5<-rep(name5,3)
name6<-"Annual_1"
name6<-rep(name6,3)
}
else if(input$period=="4 years"){
base[[i]] <- forecast(fcastsFUN(aggts_total[[i]]), h=4*frequency(aggts_total[[i]]))
name1 <- c("Week_1")
for(i in 2:52){
names11<-paste("Week_",i)
name1<-c(name1,names11)
}
name1<-rep(name1,4)
name2 <- c("BiWeek_1")
for(i in 2:26){
names12<-paste("BiWeek_",i)
name2<-c(name2,names12)
}
name2<-rep(name2,4)
name3 <- c("Month_1")
for(i in 2:13){