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Note: This is an optional project which has been given for you to self-check your understanding of the prior concepts. We do not have the provision for manual assessment of the same.
Forecast sales quantities of each store and each product.
Input data available:
Historical sales values (Location: /cxldata/datasets/project/sales_historical_sales_value.csv)
Sales value (dependent)
Historical Disposable Personal Income values (Location: /cxldata/datasets/project/sales_disposable_personal_income.csv)
Disposable Personal Income value
Additional features that can be computed are:
Disposable Personal Income: As a leading indicator, this index changes before sales change. Observe the best lag that is of interest.
Modeling parameters, including test.length, seasonality, observation.freq, and timeformat, needs to be input as well.
Date features: year, month, week of month, etc.
Holiday features: New Year, U.S. Labor Day, U.S. Thanksgiving, Cyber Monday, Christmas, etc.
Consider only sales values greater than 20
Divide the dataset into 2 years of training set and last 1 year of test set.
Take a log transformation of the sales value (dependent variable)
Please use the forum below to discuss the problem and post queries.
Data source Acknowledgement: This dataset is taken from the UCI machine learning repository Azure-Blog-Storage-Template Data, and disposable income is taken from https://fred.stlouisfed.org/
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