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Data Science Improving Forecasting Methods
Submission Deadline: Dec. 10, 2019

This special issue currently is open for paper submission and guest editor application.

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Lead Guest Editor
Marco Bouzada
Estacio de Sá University, Rio de Janeiro, Brazil
Guest Editors
  • Paulo Roberto Da Costa Vieira
    Department of Business, Universidade Estácio de Sá, Rio de Janeiro, Brazil
  • Antônio Silva
    Estácio de Sá University, Rio de Janeiro, Brazil
  • Eduardo Camilo-da-Silva
    Federal Fluminense University, Rio de Janeiro, Brazil
  • Veranise Dubeux
    ESPM, São Paulo, Brazil
  • Claudio Barbedo
    IBMEC, Rio de Janeiro, Brazil
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=367). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.

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Special Issue

Introduction
Forecasting methods can help companies to, among other things: plan and estimate the values of the investments to be made, the inventories to be created, the service capacity needed to provide a service, the size of the production, etc .; verify the effect of the entry of competitors; find out where it is necessary to make a sales effort and have different schedules of promotions and discounts. As a result of the forecasting process, overestimated estimates (forecast above the real) entail the so-called excess cost, which can manifest itself in the form of, for example, a higher fixed cost, an unnecessary cost of inventory, obsolescence, perishability, or an expense advertising. On the other hand, underestimated estimates (forecast below the real) cause the so-called stockout cost, which can take the form of, for example, loss of the contribution margin of a product or service, waiting orders in the production queue or negative consequences of a demand not answered. Therefore, the ideal is to make forecasts that are as close as possible to reality, that is, to create models that have the least possible prediction errors. With the exponentially increasing amount of information available to enterprises to use, the challenge of forecasting methods becomes even greater and more important, both computationally and analytically. This special issue aims to contribute to the explanation of how Data Science can help Forecasting Methods within this current Big Data scenario.

Aims and Scope:

  1. Forecasting Methods
  2. Data Science
  3. Big Data
  4. Forecasting models
  5. Forescasting errors
  6. Models improvement
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