This is a package to create simulated data that can be used for developement of statistical models for Demand Forecasting, i.e., time series predictions for different products in different locations, for example of a grocery chain.
Just run simulations.py, which includes the main function.
There are three different data sets:
train.csv: Meant to be used for the training of the statistical models.
test.csv: Can be considered as future data and is meant to be predicted out-of-sample (i.e., aim is to achieve generalization in the training).
test_results.csv: Contains the actual values to be predicted from test.csv. Only meant for model evaluation and must not be used in the training or prediction processes.
P_ID Identifier of the different products/articles.
L_ID Identifier of the different locations/stores.
DATE Date of the data of the given record. Together with P_ID and L_ID, uniquely identifies each record.
SALES Aggregated daily sales of the given product in the given location at the given date.
PG_ID_1 Identifier of the highest level of the product group hierarchy, e.g., food or beverages.
PG_ID_2 Identifier of the second highest level of the product group hierarchy, e.g., sweets or vegetables.
PG_ID_3 Identifier of the lowest level of the product group hierarchy, e.g., apples or bananas.
NORMAL_PRICE Usual, unreduced price of the product-location-date combination at hand. Not necessarily the actual sales price (in case of a price reduction).
SALES_PRICE Actual, potentially reduced, price of the product-location-date combination at hand.
PROMOTION_TYPE Values indicating no promotion (0) or two different typess of promotions (1 and 2) for the product-location-date combination at hand.
SALES_AREA Size of the given store in square meters.
EVENT Name of a holiday occuring on the given date in the region of the given location.
SCHOOL_HOLIDAY Boolean flag indicating if the given date is a school holiday in the region of the given location.
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