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m5

M5 Walmart Challenge Data

Installation

You can install the development version of m5 from GitHub with:

# install.packages("devtools")
devtools::install_github("krzjoa/m5")

Usage

library(m5)
library(zeallot)
library(ggplot2)

DIR <- 'data'

# Downloading the data
m5_download(DIR)

# Loading the data
c(sales_train,
   sales_test,
   sell_prices,
   calendar,
   weights) %<-% m5_get_raw_evaluation(DIR)
   
# Preparing the data
m5_data  <-
   m5_prepare(sales_train, sales_test, calendar, sell_prices)

# Demand classification
m5_demand <- m5_demand_type(m5_data)

foods_1_demand <- 
  m5_demand[startsWith(as.character(m5_demand$item_id), "FOODS_1")]

plot <-
  ggplot(foods_1_demand) +
  geom_point(aes(log(cv2), log(adi),
                 item_id = item_id, col = demand_type)) +
  geom_hline(yintercept = log(1.32)) +
  geom_vline(xintercept = log(0.49)) +
  theme_minimal()

plot

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Version

Install

install.packages('m5')

Monthly Downloads

266

Version

0.1.1

License

MIT + file LICENSE

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Last Published

September 12th, 2022

Functions in m5 (0.1.1)

m5-package

m5: 'M5 Forecasting' Challenges Data
m5_prepare

Prepare the ready-to-use M5 data in one data.frame
m5_get_raw

m5_download

Download and unzip the raw data to the specified directory
m5_demand_type

Classify time series of the particular items
tiny_m5

A subset from M5 Walmart Challenge Dataset in one data frame