library(tidyverse)
library(forecast)

source("Scripts/Functions.r")
#source("Scripts/Load_Wyoming_Web_Data.r")

DF <- FRED_GET('WYLINC3POP','LN_POP') %>% select(-YEAR)

TS <- 1000*ts(DF,start=c(1970),end=c(2024),frequency=1)
BC <- BoxCox.lambda(TS)
MODEL <- auto.arima(TS, lambda = BC)
forecast(MODEL,h=20)
plot(forecast(MODEL,h=35),main="Lincoln County Population Forecast")
####Employment to pop ratio
EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP)
EMP <- EMP %>% mutate(RATIO=LN_POP/EMP)
ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line()
AVG_POP_RATIO <- mean(EMP$RATIO)
SD_POP_RATIO <- sd(EMP$RATIO)

#####Plan and ideas
#1) Review IMPLAN for industry multipliers
#2) Review IMPLAN for employment to population multipliers (imparted)
#3) Find a list of all planned new projects
#4) Use the IMPLAN multipliers for each sector to estimate total change
#5) Develop survey to estimate likelihood of new projects
#6) Compare to the ARMA percentile
#7) Adjust the ARMA up assuming some of these outputs are known.
#8) Occupancy rate from IMPLAN as a housing cap when projecting
#9) Model housing construciton rate (Maybe) 
#10) Employment rate by age in IMPLAN
####Other ideas, develop larger plan? Maybe look at decline in other industries as a proportion of employment

###Seperate out Kemmer and Diamondville? http://eadiv.state.wy.us/pop/wyc&sc40.htm