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

5 exports 2 stars 0.85 score 6 dependencies 28 scripts 321 downloads

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

TargetResultDate
Doc / VignettesOKSep 01 2024
R-4.5-winWARNINGSep 01 2024
R-4.5-linuxWARNINGSep 01 2024
R-4.4-winWARNINGSep 01 2024
R-4.4-macWARNINGSep 01 2024
R-4.3-winWARNINGSep 01 2024
R-4.3-macWARNINGSep 01 2024

Exports:m5_demand_typem5_downloadm5_get_raw_evaluationm5_get_raw_validationm5_prepare

Dependencies:cpp11data.tablegenericslubridatestringitimechange

Demand classification

Rendered fromdemand_classification.Rmdusingknitr::rmarkdownon Sep 01 2024.

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