Kaggle-Walmart Sales Forecasting

This repository hosts R code for the winning entry in Kaggle's Walmart sales forecasting competition.

Description

There is a Kaggle forum post explaining the winning entry. Briefly, it is an unweighted average of 6 component models, all of them weekly time-series models, followed by a transformation around Christmas to reflect that the day of the week that Christmas lands on shifts from year to year. That shift is explained here.

Requirements

• R
• R packages forecast, plyr, and reshape - use install.packages('forecast'), etc. from within R
• Walmart data - You will need train.csv, test.csv, and sampleSubmission.csv. You may have to accept a set of competition rules to get the data.

Setup

The code assumes a directory structure under the project level of:

• data/ - with the data from Kaggle
• R/ - with the code from here
• submissions/ - the code will write submissions here

Usage:

You can regenerate the winning entry with source('runAll.R') from inside R. Without the fourier.arima model, the code runs in 30-40 minutes and gets about 2320 on the final leader board. With that model included, it generates the winning score of around 2300, but takes an extra 2 hours or so.

To run the best performing single model from scratch:

source('util.R')
source('grouped.forecast')
source('util')
train <- raw.train()
test <- raw.test()
pred <- grouped.forecast(train, test, 'stlf.svd', model.type='ets', n.comp=12)
pred <- postprocess(train, test, shift=2.5)
write.submission(pred)

If you are in the competition (or have accepted the rules), you should be able to submit that for 2348 on the final leaderboard by going to: submit and uploading the .csv file.