I have this dataset that contains multiple series (50 products). My dataset has 50 products (50 columns). each column contains the monthly sales of a product. I recently learned about bootstrap and how it can improve forecast accuracy. Therefore, I decided to compare the results that I will get when using ets, Arima, and when using bootstrapping method. Here is my code and I would love if someone can help me understand how to apply bootstrap on a time series and how to use it with other forecasting techniques. So far I've used
Arima without bootstrap and now I want to use bootstrapping and then compare the results of each method and prove which one is the best method to forecast time series.
library(fable) library(dplyr) library(tidyr) library(ggplot2) y <- ts(matrix(rnorm(175*50), ncol=50), frequency=12, start=c(2007,1)) %>% as_tsibble() %>% rename(Month = index, Sales=value) fit.ets <- y %>% model(ETS(Sales)) fit.ets f.ets <- forecast(fit.ets, h=12) f.ets