Forward forecasts using rEDM

gistfile1.txt

library(rEDM)

#### Example 1: a single forward prediction

# Assume we have already done the analysis, finding that the best embedding 
#   dimension is 2. Our objective is to make multiple 1-step ahead forecasts 
#   using simplex and E = 2.
dat <- data.frame(yr = as.numeric(time(sunspot.year)), 
                  sunspot_count = as.numeric(sunspot.year))
E <- 3

# setup data structures:
# 1) add empty row with incremented time, and empty variable of interest
dat <- rbind(dat, 
             data.frame(yr = tail(dat, 1)$yr + 1, 
                        sunspot_count = NA))
# 2) setup lib and pred:
#    lib excludes the last row; 
#    pred includes the last row and E previous lags
lib <- c(1, NROW(dat) - 1)
pred <- c(NROW(dat) - E, NROW(dat))

# 3) make prediction
simplex_output <- simplex(dat, lib = lib, pred = pred, E = E, 
                          stats_only = FALSE)

# 4) replace the missing value in `dat` with the forecast, so that we can 
#    repeat the process
dat[NROW(dat), "sunspot_count"] <- simplex_output$model_output[[1]][E, "pred"]

#### Example 2: multiple forward predictions

# setup initial data and E as previously
dat <- data.frame(yr = as.numeric(time(sunspot.year)), 
                  sunspot_count = as.numeric(sunspot.year))
E <- 3

# wrap up previous steps 1-4 in a loop

num_predictions <- 5

for (i in seq(num_predictions))
{
    dat <- rbind(dat, 
                 data.frame(yr = tail(dat, 1)$yr + 1, 
                            sunspot_count = NA))

    lib <- c(1, NROW(dat) - 1)
    pred <- c(NROW(dat) - E, NROW(dat))
    
    simplex_output <- simplex(dat, lib = lib, pred = pred, E = E, 
                              stats_only = FALSE)
    
    dat[NROW(dat), "sunspot_count"] <- simplex_output$model_output[[1]][E, "pred"]
}