Time Series Analysis and Forecasting

Time Series Analysis and Forecasting

Public courses

£1750

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

Private courses

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

Time Series Forecasting is an important component of the decision-making process in every business. The ability to forecast accurately in a changing business environment is crucial to the success of investments or to meet customer demand. Unfortunately, in practice, we often see inaccurate forecasts with large errors due to improper use of forecasting techniques. Costs incurred by companies due to this inaccuracies are significant and yet very easy to minimize with the application of proper forecasting methods. It is absolutely crucial to learn and properly understand methods used in the predictive analysis, as there is no uniform approach to forecasting.

Time Series Analysis and Forecasting Training will answer many questions and doubts you may encounter during the process of forecasting. During this training you will learn about every step of forecasting process starting with defining the problem, collecting data and making preliminary analysis through choosing the right model and estimation method, and ending with model quality and forecast accuracy assessment. Every aspect of this training is supported by numerous examples and exercises in an appropriate Time Series Analysis software. Since your ability to apply learnings from this training in practice is vital to us we will conduct this training in a software of your choice.

The recommended software for this training is R, which is a very popular free tool with a versatile usage in data analysis. Regardless of the software used during the training, you will receive a set of codes, procedures, and commands to be executed during each step in order to create a reliable forecast. You will also receive a copy of materials presented during the training and a certificate of completion.

The training is carried out by experts – graduates of prestigious universities, lecturers with extensive experience in the field of time series analysis and forecasting in business. Our trainers gained their modeling experience working for governments, international organizations, and corporations.

Who is this training for?

The training is aimed primarily at people whose work requires professional analysis of time series and forecasting, in particular:

  • Analysts
  • Consultants
  • Statisticians
  • Managers

What will I learn?

After completing the training, participants will be able to:

  • Use the techniques used in the forecasting process
  • Decompose time series
  • Remove seasonality from time series
  • Use exponential smoothing methods for the analysis and forecasting
  • Use advanced econometric models for forecasting time series
  • Create forecasts based on the linear regression model
  • Create point and interval forecasts
  • Use qualitative techniques to modify quantitative forecasts
  • Examine the quality of predictive models allowing for improvement of tools and forecasts

Course outline

  1. Introduction
    • Forecasting methods
    • Forecasting process
    • Forecast, Plan, Goal
    • Overview of the tools used for Time Series Analysis
  2. Basic concepts of Statistical Inference
    • Random variables
    • Stochastic process vs Time Series
    • Statistical tests
    • Exercises
  3. Time series characteristics
    • Graphical analysis
    • Stationarity
    • Transforming Time Series
    • Exercises
  4. Forecast accuracy
    • Point and interval forecast
    • Ex -post forecast
    • Ex-ante forecast
    • Forecast stability analysis
    • Forecast accuracy measures
    • Exercises
  5. Simple forecasting methods
    • Naïve method
    • Average method
    • Moving average
    • Naïve method with trend and seasonality
    • Exercises
  6. Time series decomposition
    • Time Series components
    • Trend analysis and forecasting
    • Seasonal adjustment
    • Arima X12
    • Exercises
  7. Exponential smoothing
    • Simple exponential smoothing
    • Holt method
    • Holt-Winters method
    • Damped trend method
    • Variable selection
    • Exponential smoothing in state space models
    • Exercises
  8. Linear models in Time Series forecasting
    • Correlation and regression
    • Estimation
    • Variable selection
    • Model validation
    • Forecasting
    • Exercises
  9. ARIMA models
    • Analysis of AR and MA components
    • Box-Jenkins model identification method
    • Model validation
    • Forecasting
    • Exercises
  10. Forecasting hierarchical time series
    • Bottom-up approach
    • Top-down approach
    • Middle out approach
    • Optimal approach combination
    • Exercises
  11. Forecasting method selection
    • Forecast stability
    • Automation of forecasting method selection
    • Forecast averaging
    • Exercises
  12. Qualitative forecasting methods
    • Limitations
    • Judgmental forecasts
    • Forecast by analogy
    • Delphi method
    • Scenario forecasting
    • New product forecasting
    • Exercises

Course Curriculum

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