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Class Simulation and Forecasting Techniques

  • Presentation

    Presentation

    Provides basic knowledge, skills and essential tools of simulation and prediction.

  • Code

    Code

    ULHT41-22920
  • Syllabus

    Syllabus

    • Uncertainty and risk.
    • Theoretical probability distributions.
    • Basic concepts of simulation.
    • Simulation methods.
    • Simulation models.
    • Scenario analysis.
    • Basic concepts of prediction.
    • Predictive analytics techniques.
    • Regression analysis.
    • Statistical classification.
    • Time series.
  • Objectives

    Objectives

    Understanding the differences between uncertainty and risk. Analyzing the behavior of systems that cannot be operationalized through actual experiments and predict their likely future behavior. Modeling and simulating systems. Determining risk from the dispersion derived from the estimation of the range of values that a target variable might take on a model. Understanding and applying diverse predictive methods. The student should acquire skills and competences allowing him or her to model, simulate, and predict real-world systems through analytical techniques. He or she should understand and apply the most relevant computational methods of simulation and prediction, know their relative advantages and drawbacks, model problems, get to know existing software for that purpose, and master how to apply them in practical problems.

  • Teaching methodologies and assessment

    Teaching methodologies and assessment

    The course includes the use of interactive simulations, up-to-date case studies and participatory methods that encourage practical application and critical thinking on the part of the students. The aim is to promote dynamic and engaging learning, preparing students to face complex real-world challenges in the context of using techniques of this nature.

  • References

    References

    • Assis, Rui - EXCEL na Simulação de Sistemas e Análise de Risco. Edição de autor, 2014. ISBN: 978-989-20-4412-5.
    • Camm, Cochran, Fry, Ohlmann e Anderson - Business Analytics. 3.ª edição. Cengage Learning, 2018. ISBN: 978-1-337-40642-0.

     

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