Forecasting using mutiple seasonal STL and arima - time-series

Question

I am attempting to forecast half hourly electricity data. The method I am using is to decompose the electricity consumption data using 'mstl' from the 'Forecast' package by Rob Hyndman and then forecast the seasonally adjusted data using ARIMA.
df <- IntervalData %>% select(CONSUMPTION_MW)
length_test_set = 17520
h = 17520
# create msts object with daily, weekly and monthly seasonality
data_msts <- msts(df, seasonal.periods=c(48,48*7,365/12*48))
train_msts = msts(df[1:(nrow(df)-length_test_set),],seasonal.periods=c(48,48*7,365/12*48))
test_msts = msts(df[((nrow(df)-length_test_set)+1):(nrow(df)),],seasonal.periods=c(48,48*7,365/12*48))
fit_mstl = mstl(train_msts, iterate = 4, s.window = 19, robust = TRUE)
fcast_arima=forecast(fit_mstl,method='arima',h=h)
How do I specify the order of my ARIMA model eg. ARIMA(2,1,6)?

Answer 1

You will need to write your own forecast function like this (using fake data so it can be reproduced).
library(forecast)
df <- data.frame(y=rnorm(50000))
length_test_set <- 17520
h <- 17520
# create msts object with daily, weekly and monthly seasonality
data_msts <- msts(df, seasonal.periods = c(48, 48*7, 365/12*48))
train_msts <- msts(df[1:(nrow(df) - length_test_set), ], seasonal.periods = c(48, 48 * 7, 365 / 12 * 48))
test_msts <- msts(df[((nrow(df) - length_test_set) + 1):(nrow(df)), ], seasonal.periods = c(48, 48 * 7, 365 / 12 * 48))
fit_mstl <- mstl(train_msts, iterate = 4, s.window = 19, robust = TRUE)
# Function to fit specific ARIMA model and return forecasts
arima_forecast <- function(x, h, level, order, ...) {
fit <- Arima(x, order=order, seasonal = c(0,0,0), ...)
return(forecast(fit, h = h, level = level))
}
# Example using an ARIMA(3,0,0) model
fcast_arima <- forecast(fit_mstl, forecastfunction=arima_forecast, h = h, order=c(3,0,0))
Created on 2020-07-25 by the reprex package (v0.3.0)