Market forecasting

Market forecasting

Public courses


- Anyone can join the training
- Course outline as presented on the website
- Small groups, 3-10 people


Price set individually

- Training workshop just for your team
- You choose date and location of the training
- Course outline tailored to your needs

About the training

This training is a practical approach to common business forecasting problems such as demand, supply and inventory forecasting. During the training you will learn to use both basic and advanced techniques used in predictive modelling. The theory is explained in an intuitive way that is easy to understand. You will learn how to apply each method in practice.

This course is conducted by expert trainers with vast business forecasting experience. We focus on the practical side of the training and show real-life problem examples. The last part of the course is a forecasting project that lets you practice and polish the knowledge gained during the training.

Who is this training for?

This training has been designed for people responsible for preparation of business forecasts or interested in introducing such process to their organization. It is a great choice for both big companies with established forecasting processes, as well as, smaller companies that want to start applying a quantitative approach to their predictions.

What will I learn?

After completing the training, you will be able to:

  • Analyze time series and create quality forecasts
  • Manage the forecasting process
  • Identify and gather useful data
  • Build predictive models
  • Use advanced forecasting methods
  • Choose the right forecasting method for a problem
  • Combine quantitative forecasts with expert knowledge
  • Create simulations to assess risks

Course outline

  1. Introduction to Market Forecasting
    • What can be forecasted?
    • Statistics, Econometrics, Forecasting
    • Forecast + Target = Plan
    • Forecast accuracy, sources of forecast errors
    • Types of forecasts, data types
    • Common mistakes
    • Steps of forecasting process
    • Forecasts role in business
    • Quantitative vs qualitative forecasting
    • Presenting and visualizing forecasts effectively
    • Disussion – common forecasting problems
  2. Selected market forecasting problems
    • Forecasting sales
    • Forecasting supply
    • Forecasting inventory
    • Forecasting finances
  3. Forecasting essentials
    • Forecasting software – pros and cons
    • Basic statistics
    • Preliminary visual data analysis
    • Simple forecasting methods
    • Measuring forecast accuracy and evaluation of forecasting method
    • Examples and exercises
  4. Data types and sources of data; where to find economic data
    • Time series, panel data and cross-sectional data
    • Basic sources of national economic data
    • External data sources, export markets
    • Private sector demand and supply data sources
    • Searching for data in the web
    • Examples and exercises
  5. Quantitative forecasting methods
    • Introduction to forecasting techniques
    • Searching for patterns in data
    • Moving averages, time series smoothing methods
    • Time series – characteristics, decomposition, seasonality
    • Linear regression for time series and cross-sectional data
    • ARIMA – forecasting using lagged values
    • Assesing forecast quality
    • Combining forecasts from different models
    • Examples and exercises
  6. Qualitative forecasting methods
    • Limitaions and challenges in qualitative methods
    • Basic rules of qualitative forecasting
    • Surveys
    • Forecast by analogy
    • Scenario analysis
    • New product forecasting
    • Delphi method
    • Combining qualitative and quantitative forecasts
    • Examples and exercises
  7. Advanced forecasting methods
    • Dynamic regression models
    • VAR models
    • Neural networks
    • Hierarchical time series forecasting
    • Examples and exercises
  8. Forecasting with simulations
    • Introduction to simulation methods
    • Microeconomic approach to forecasting
    • Creating simulation model
    • Assumptions – probability distribution
    • Generating foreacast and confidence intervals
    • Examples and exercises

Course Curriculum

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