In JSzitas/soothsayer: Tidy Time Series Forecast Model Selection

knitr::opts_chunk$set(echo = TRUE)

Fit forecasts

source("../R/forecast.R")
library(magrittr)

experiment_gen <- coro::generator(function() {
  x <- 1
  while(TRUE) {
    coro::yield(x)
    x <- x + 1
  }
})
experiment_n <- experiment_gen()
experiment_run <- experiment_n()

part <- 2

M_all_part <- qs::qread(paste0("../data/M_all_",part,".qs"))

ts_train_test(M_all_part) -> train_test

future::plan("multisession")

models <- fit_models( train = train_test[["train"]],
            models = list( arima = fable::ARIMA,
                           fable::SNAIVE,
                           fable::RW,
                           theta = fable::THETA,
                           ets = fable::ETS,
                           nnetar = fable::NNETAR,
                           croston = fable::CROSTON,
                           ar = fable::AR))

qs::qsave( models, file = paste0( "models_",part,"_", Sys.Date(),
                                      "_",
                                      experiment_run, ".qs" ),
           nthreads = 12)

forecast_models( train_test[["test"]], models[["models"]] ) -> forecasts
qs::qsave( forecasts, file = paste0( "forecasts_",part,"_", Sys.Date(),
                                  "_",
                                  experiment_run, ".qs" ),
           nthreads = 12)

accuracies <- fabletools::accuracy(forecasts[["forecasts"]], train_test[["test"]])

qs::qsave( accuracies, file = paste0( "accuracies_",part,"_", Sys.Date(),
                                     "_",
                                     experiment_run, ".qs" ),
           nthreads = 12)

JSzitas/soothsayer documentation built on April 18, 2023, 12:59 a.m.