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intuitive_reconciliation.R
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intuitive_reconciliation.R
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library(tidyverse)
library(magrittr)
library(future)
library(forecast)
library(doParallel)
library(foreach)
library(parallel)
# Setup
data_label <- commandArgs(trailingOnly = TRUE) # "simulation", "corr_i", "tourism_i", or "labour_i"
nlambda <- 20
MonARCH <- TRUE
workers <- parallelly::availableCores()
source("R/intuitive_reconcile.R")
# Utility function
reconcile_forecast <- function(index, fits, train, basefc, resids, test, S, nvalid,
method, method_name, nlambda,
deteriorate = FALSE, deteriorate_series, deteriorate_rate,
MIPFocus, Cuts, TimeLimit,
MIPVerbose, MonARCH, workers){
n <- NCOL(fits)
fitted_values <- fits[fits$Index == index, -n] |> as.matrix()
residuals <- resids[resids$Index == index, -n] |> as.matrix()
train_data <- train[train$Index == index, -n] |> as.matrix()
base_forecasts <- basefc[basefc$Index == index, -n] |> as.matrix()
if (deteriorate){
fitted_values[, deteriorate_series] <- fitted_values[, deteriorate_series]*deteriorate_rate
base_forecasts[, deteriorate_series] <- base_forecasts[, deteriorate_series]*deteriorate_rate
residuals <- train_data - fitted_values
}
Base <- list(y_tilde = base_forecasts, G = NA, z = NA, lambda_report = NA)
BU <- intuitive.reconcile(base_forecasts = base_forecasts, S = S, method = "bu",
MonARCH = MonARCH, workers = workers)
for(i in 1:length(method)){
assign(method_name[i],
intuitive.reconcile(base_forecasts = base_forecasts, S = S, method = method[i],
residuals = residuals, MonARCH = MonARCH, workers = workers))
assign(paste0(method_name[i], "_intuitive"),
intuitive.reconcile(base_forecasts = base_forecasts, S = S,
method = method[i], residuals = residuals,
fitted_values = fitted_values, train_data = train_data, nvalid = nvalid,
subset = TRUE, nlambda = nlambda,
MIPFocus = MIPFocus, Cuts = Cuts, TimeLimit = TimeLimit,
MIPVerbose = MIPVerbose, MonARCH = MonARCH, workers = workers))
print(paste("===", method_name[i], "finished!"))
}
mget(c("Base", "BU", method_name, paste0(method_name, "_intuitive")))
}
#################################################
# Data
#################################################
#----------------------------------------------------------------------
# Simulation data
## Total/Middle/Bottom: 3 levels, n = 7
## Training set: 1978Q1-2018Q4
## Test set: 2019Q1-2022Q4
#----------------------------------------------------------------------
if (data_label == "simulation"){
nvalid <- 16; MIPFocus = 0; Cuts = -1; TimeLimit = 600; MIPVerbose = FALSE
method <- c("ols", "wls_struct", "wls_var", "mint_cov", "mint_shrink")
method_name <- c("OLS", "WLSs", "WLSv", "MinT", "MinTs")
}
#----------------------------------------------------------------------
# Simulation data - correlation
## Total/Middle/Bottom: 3 levels, n = 7
## Training set: 1-100
## Test set: 1
#----------------------------------------------------------------------
if (grepl("corr", data_label)){
nvalid = NULL; MIPFocus = 0; Cuts = -1; TimeLimit = 600; MIPVerbose = FALSE
method <- c("ols", "wls_struct", "wls_var", "mint_cov", "mint_shrink")
method_name <- c("OLS", "WLSs", "WLSv", "MinT", "MinTs")
}
#----------------------------------------------------------------------
# Australian domestic tourism (only considering hierarchical structure)
##
## Monthly series from 1998Jan-2017Dec: 240 months (20 years) for each series
##
## Total/State/Zone/Region: 4 levels, n = 111 series in total
##
## Training set: 1998Jan-2016Dec
## Test set: 2017Jan-2017Dec
#----------------------------------------------------------------------
if (grepl("tourism", data_label)){
nvalid <- 12; MIPFocus = 3; Cuts = 2; TimeLimit = 600; MIPVerbose = FALSE
method <- c("ols", "wls_struct", "wls_var", "mint_shrink")
method_name <- c("OLS", "WLSs", "WLSv", "MinTs")
}
#----------------------------------------------------------------------
# ABS - Unemployed persons by Duration of job search, State and Territory
##
## 6291.0.55.001 - UM2 - Unemployed persons by Duration of job search, State and Territory, January 1991 onwards
##
## Monthly series
## Duration of job search (Duration, 6) * State and territory (STT, 8): n = 63 series in total, nb = 48 series at the bottom level
##
## Training set: 2010Jan-2022Jul
## Test set: 2022Aug-2023Jul
#----------------------------------------------------------------------
if (grepl("labour", data_label)){
nvalid = 12; MIPFocus = 3; Cuts = 2; TimeLimit = 600; MIPVerbose = FALSE
method <- c("ols", "wls_struct", "wls_var", "mint_shrink")
method_name <- c("OLS", "WLSs", "WLSv", "MinTs")
}
#################################################
# Import base forecast results
#################################################
for (i in c("S", "fits", "resids", "train", "basefc","test")){
assign(i, readRDS(file = paste0("data/", data_label, "_", i, ".rds")))
}
indices <- unique(fits$Index)
#################################################
# Reconcile forecasts
#################################################
reconsf <- indices |>
purrr::map(\(index) reconcile_forecast(index, fits, train, basefc, resids, test, S, nvalid,
method, method_name, nlambda,
deteriorate = FALSE,
MIPFocus = MIPFocus, Cuts = Cuts, TimeLimit = TimeLimit,
MIPVerbose = MIPVerbose, MonARCH = MonARCH, workers = workers))
saveRDS(reconsf, file = paste0("data_new/", data_label, "_intuitive_reconsf.rds"))
rm(reconsf)
print("Reconciliation finished!")
#################################################
# Reconcile forecasts - deteriorate base forecasts for some time series
#################################################
if (data_label == "simulation"){
scenario <- c("s1", "s2", "s3")
deteriorate_series <- c("AA", "A", "Total")
deteriorate_rate <- rep(1.5, 3)
for (i in 1:length(scenario)){
reconsf_s <- indices |>
purrr::map(\(index) reconcile_forecast(index, fits, train, basefc, resids, test, S, nvalid,
method, method_name, nlambda,
deteriorate = TRUE,
deteriorate_series = deteriorate_series[i],
deteriorate_rate = deteriorate_rate[i],
MIPFocus = MIPFocus, Cuts = Cuts, TimeLimit = TimeLimit,
MIPVerbose = MIPVerbose, MonARCH = MonARCH, workers = workers))
saveRDS(reconsf_s, file = paste0("data_new/", data_label, "_intuitive_reconsf_", scenario[i], ".rds"))
rm(reconsf_s)
print(paste("Scenario", i, "finished!"))
}
}