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XuetangX

Forecasting and Decision Making

Shenyang University Of Technology via XuetangX

Overview

In a world characterized by rapid change and uncertainty, the ability to forecast future trends and make effective decisions is invaluable. Whether you're a business professional, an aspiring entrepreneur, or a student preparing for a career, mastering the art of forecasting and decision making equips you with essential skills to navigate complex situations and seize opportunities. This course offers a comprehensive foundation in the principles and techniques of forecasting and decision making, empowering you to make informed choices and drive success in various domains.

Our course delves deep into the realms of forecasting and decision making, offering a comprehensive exploration of key concepts and methodologies. From qualitative forecasting, quantitative forecasting, decision analysis to multicriteria decisions, you'll gain a holistic understanding of the factors influencing future outcomes and the strategies to navigate them effectively.

Theory meets practice in our course, as we emphasize hands-on learning experiences that translate theoretical knowledge into actionable insights. Through case studies, simulations, and real-world scenarios, you'll sharpen your analytical skills, hone your decision-making acumen, and emerge ready to tackle the challenges of today and tomorrow with confidence.

By completing this course, you'll emerge with the knowledge, skills, and confidence to excel in a variety of roles and industries. Whether you're seeking career advancement, starting your own business, or simply aiming to enhance your decision-making abilities, this course will provide you with a solid foundation for success in today's dynamic world. Invest in your future by enrolling in our “Forecasting and Decision Making” course and unlock your potential for greater achievement.

Syllabus

  • Chapter 1 Summary of Forecasting
    • 1.1 What can be forecast?
    • 1.2 Forecasting, planning and goals
    • 1.3 Forecasting data and methods
    • 1.4 The basic steps in a forecasting task
    • 1.5 The statistical forecasting perspective
  • Chapter 2 The Forecaster’s Toolbox
    • 2.1 Some simple forecasting methods
    • 2.2 Residual diagnostics
    • 2.3 Evaluating forecast accuracy
    • 2.4 Prediction intervals
  • Chapter 3 Judgmental Forecasts
    • 3.1 Beware of limitations
    • 3.2 Key principles
    • 3.3 The Delphi method
    • 3.4 Forecasting by analogy
    • 3.5 Scenario forecasting
    • 3.6 New product forecasting
    • 3.7 Judgmental adjustments
  • Chapter 4 Time Series Regression Models
    • 4.1 The linear model
    • 4.2 Least squares estimation
    • 4.3 Evaluating the regression model
    • 4.4 Nonlinear regression
    • 4.5 Correlation, causation and forecasting
  • Chapter 5 Time Series Decomposition
    • 5.1 Time series components
    • 5.2 Moving averages
    • 5.3 Classical decomposition
    • 5.4 STL decomposition
  • Chapter 6 Exponential Smoothing
    • 6.1 Simple exponential smoothing
    • 6.2 Trend methods
    • 6.3 Holt-Winters’ seasonal method
  • Chapter 7 ARIMA Models
    • 7.1 Stationarity and differencing
    • 7.2 Backshift notation
    • 7.3 Autoregressive models
    • 7.4 Moving average models
    • 7.5 Non-seasonal ARIMA models
  • Chapter 8 Decision Analysis
    • 8.1 Problem formulation
    • 8.2 Decision making without probabilities
    • 8.3 Decision making with probabilities
    • 8.4 Risk analysis and sensitivity analysis
    • 8.5 Decision analysis with sample information
    • 8.6 Computing branch probabilities with Bayes’ theorem
    • 8.7 Utility theory
  • Chapter 9 Multicriteria Decisions
    • 9.1 Goal programming: formulation and graphical solution
    • 9.2 Scoring models
    • 9.3 Analytic Hierarchy Process
    • 9.4 Establishing priorities using AHP
    • 9.5 Using AHP to develop an overall priority ranking
  • Final Test

    Taught by

    Qi Huang and Qiang Hou

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