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Forecasting
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Sales Forecasting in the Big Data Era
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Sales Forecasting in the Big Data Era

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (1 September 2021) | Viewed by 8195

Special Issue Editor

Dr. Sébastien Thomassey

E-Mail Website
Guest Editor
ENSAIT / GEMTEX, 2 allée Louise et Victor Champier, 59100 Roubaix, France
Interests: sales forecasting; machine learning; multi-criteria decision making; multi-objective optimization; clustering; classification; supply chain management

Special Issue Information

Dear Colleagues,

Sales forecasting is a crucial issue for many companies. Production planning and scheduling, supply chain optimization, etc. often rely on sales forecasting systems. The most common approach to deal with sales forecasting is based on statistical times series methods, and obtains satisfactory results in different situations. However, in many activities, fluctuating demand, multiple exogenous factors, and high product variety with short historical sales make the implementation of time series methods very complex.

With the current emergence of mobile and connected technologies, a huge number of data—that is, big data—are now available. This data provides valuable information about consumer behavior, feeling, sentiments, product features, real time localization, etc. This big data era is a real opportunity to enhance sales forecasting systems. To deal with this massive amount of information, many artificial intelligence techniques have been developed. Thus, sales forecasting systems should now integrate different techniques to deal with the heterogeneous data and specific constraints of the current environment.

This Special Issue aims to provide a broad overview of current advanced sales forecasting techniques to academics and practitioners.

Dr. Sébastien Thomassey
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sales forecasting
  • big data
  • artificial intelligence
  • machine learning
  • times series

Published Papers (1 paper)

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Research

19 pages, 898 KiB  
Article
Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices
by Lorenzo Menculini, Andrea Marini, Massimiliano Proietti, Alberto Garinei, Alessio Bozza, Cecilia Moretti and Marcello Marconi
Forecasting 2021, 3(3), 644-662; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3030040 - 15 Sep 2021
Cited by 29 | Viewed by 7578
Abstract
Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we [...] Read more.
Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate. Full article
(This article belongs to the Special Issue Sales Forecasting in the Big Data Era)
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