Historical_Product_Demand
OpenML dataset with id 43624
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102449/Historical_Product_Demand.arff
Upload date: 24 March 2022
Dataset Characteristics
Number of features: 5 (numeric: 0, symbolic: 0 and in total binary: 0 )
Number of instances: 1,048,575
Number of instances with missing values: 11,239
Number of missing values: 11,239
Source: Charles Gaydon
This data only contains 5 variables of Productcode, Warehouse, ProductCategory, Date, Order_demand
I showed that it is possible, with trivial models, to lower the mean average forecasting error to only around 20 in terms of volume of command, this for 80 of the total volume ordered. This should prove that there is a predicting potential in this dataset that only waits to be exploited.
Again, I the reader wants to continue this work, he or she should use only a selection of the past months to make the forecast.
Other ideas for further development :
-- use warehouse and category data in the model;
-- predict normalized categories of order command (ex: 0 - 1 to 20 - - 100 to 120; where 100 is the historical max of a product) and use a classifier instead of a linear model.
-- check for AIC, BIC, AICc scores.
This page was built for dataset: Historical_Product_Demand
