Data for: A machine learning approach for forecasting hierarchical time series

Published: 2 May 2021| Version 1 | DOI: 10.17632/njdkntcpc9.1
Contributors:
Paolo Mancuso,
Veronica Piccialli,
Antonio M. Sudoso

Description

The dataset consists of 118 daily time series, representing the demand of pasta from 01/01/2014 to 31/12/2018. Besides univariate time series data, the quantity sold is integrated by information on the presence or the absence of a promotion.

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Categories

Machine Learning, Time Series, Demand Forecasting

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