Package: m5 0.1.1

m5: 'M5 Forecasting' Challenges Data

Contains functions, which facilitate downloading, loading and preparing data from 'M5 Forecasting' challenges (by 'University of Nicosia', hosted on 'Kaggle'). The data itself is set of time series of different product sales in 'Walmart'. The package also includes a ready-to-use built-in M5 subset named 'tiny_m5'. For detailed information about the challenges, see: Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilis. (2020). The M5 Accuracy competition: Results, findings and conclusions. <doi:10.1016/j.ijforecast.2021.10.009>

Authors:Krzysztof Joachimiak [aut, cre]

m5_0.1.1.tar.gz
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m5.pdf |m5.html✨
m5/json (API)

# Install 'm5' in R:
install.packages('m5', repos = c('https://krzjoa.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/krzjoa/m5/issues

Datasets:
  • tiny_m5 - A subset from M5 Walmart Challenge Dataset in one data frame

On CRAN:

data-sciencekaggle-competitionkaggle-datasetm5-competitionm5-forecastingtime-series-forecastingwalmartwalmart-sales-forecasting

4.45 score 2 stars 28 scripts 257 downloads 5 exports 6 dependencies

Last updated 2 years agofrom:8b03702c48. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winWARNINGOct 31 2024
R-4.5-linuxWARNINGOct 31 2024
R-4.4-winWARNINGOct 31 2024
R-4.4-macWARNINGOct 31 2024
R-4.3-winWARNINGOct 31 2024
R-4.3-macWARNINGOct 31 2024

Exports:m5_demand_typem5_downloadm5_get_raw_evaluationm5_get_raw_validationm5_prepare

Dependencies:cpp11data.tablegenericslubridatestringitimechange

Demand classification

Rendered fromdemand_classification.Rmdusingknitr::rmarkdownon Oct 31 2024.

Last update: 2022-09-04
Started: 2022-09-04