International Journal of Data Science and Analysis

Special Issue

Data Science Improving Forecasting Methods

  • Submission Deadline: 10 March 2020
  • Status: Submission Closed
  • Lead Guest Editor: Marco Bouzada
About This Special Issue
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
Lead Guest Editor
  • Marco Bouzada

    Estacio de Sá University, Rio de Janeiro, Brazil

Guest Editors
  • 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

  • Paulo Roberto Da Costa Vieira

    Department of Business, Universidade Estácio de Sá, Rio de Janeiro, Brazil

  • Pankaj Kumar

    Department of Computer Science and Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, India

  • Robert Mathenge Mutwiri

    Department of Mathematics, Kabarak University, Meru, Kenya