1. Chapter 2 Data Patterns and Choice of Forecasting Techniques
Business Forecasting
Chapter 2
Data Patterns and Choice of Forecasting Techniques

2. Chapter Topics Data Patterns Forecasting Methodologies
Technique Selection
Model Evaluation

3. Data Pattern and Choice of Technique
The pattern of data
The nature of the past relationship in the data
The level of subjectivity in making a forecast
All of the above help us in how we classify the forecasting technique.

4. Data Pattern and Choice of Technique
Univariate forecasting techniques depend on:
Past data patterns.
Multivariate forecasting techniques depend on
Past relationships.
Qualitative forecasts depend on:
Subjectivity: Forecasters intuition.

5. Data Patterns Data Patterns as a Guide
Simple observation of the data will show the way that data have behaved over time.
Data pattern may suggest the existence of a relationship between two or more variables.
Four Patterns: Horizontal, Trend, Seasonal, Cyclical.

6. Data Patterns Horizontal Mean
When there is no trend in the data pattern, we deal with horizontal data pattern.
Forecast Variable
Mean
Time

7. Data Patterns Trend Long-term growth movement of a time series Yt Yt t

8. Data Patterns Seasonal Pattern
A predictable and repetitive movement observed around a trend line within a period of 1 year or less.
Forecast Variable
Time

9. Data Patterns Cyclical
Occurs with business and economic expansions and contractions.
Lasts longer than 1 year.
Correlated with business cycles.

10. Other Data Patterns Autocorrelated Pattern
Data in one period are related to their values in the previous period.
Generally, if there is a high positive autocorrelation, the value in the month of June, for example, is positively related to the values in the month of May.
This pattern is more fully discussed when we talk about the Box–Jenkins methodology.

11. Measures of Accuracy in Forecasting
Error in Forecasting
Measures the average error that can be expected over time.
The average error concept has some problems with it. The positive and negative values cancel each other out and the mean is very likely to be close to zero.

12. Error in Forecasting
Mean Average Deviation (MAD)

13. Error in Forecasting
Mean Square Error (MSE)

14. Error in Forecasting
Mean Absolute Percentage Error

15. Error in Forecasting Mean Percentage Error
No bias, MPE should be zero.

16. Evaluating Reliability
Forecasters use the following two approaches to determine if the forecast is reliable or not:
Root Mean Square (RMS)

17. Evaluating Reliability
Root Percent Mean Square (R%MS)

18. Forecasting Methodologies
Forecasting methodologies fall into three categories:
Quantitative Models
Qualitative Models
Technological Approaches

19. Forecasting Methodologies
Quantitative Models
Also known as statistical models.
Include time series and regression approaches.
Forecast future values entirely on the historical observation of a variable.

20. Forecasting Methodologies
Quantitative Models
An example of a quantitative model is shown below:
= Sales one time period into the future
= Sales in the current period
= Sales in the last period

21. Forecasting Methodologies
Qualitative Models
Non-statistical or judgment models
Expert opinion
Executive opinion
Sale force composite forecast
Focus groups
Delphi method

22. Forecasting Methodologies
Technological Approach
Combines quantitative and qualitative methods.
The objective of the model is to combine technological, societal, political, and economic changes.

23. Technique Selection Forecasters depend on:
The characteristics of the decision making situation which may include:
Time horizon
Planning vs. control
Level of detail
Economic conditions in the market (stability vs. state of flux)

24. Technique Selection Forecasters depend on:
The characteristics of the forecasting method
Forecast horizon
Pattern of data
Type of model
Costs associated with the model
Level of accuracy and ease of application

25. Model Evaluation Forecasters depend on:
The level of error associated with each model.
Error is computed and looked at graphically.
Control charts are used for model evaluations.
Turning point diagram is used to evaluate a model.

26. Model Evaluation
A pattern of cumulative errors moving systematically away from zero in either direction is a signal that the model is generating biased forecasts.
Management has to establish the upper and lower control limits.
One fairly common rule of thumb is that the control limits are equal to 2 or 3 time the standard error.

27. Model Evaluation

28. Model Evaluation

29. Model Evaluation
Figure 2.7 Turning Point Analysis for Model C

30. Chapter Summary Data Patterns Forecasting Methodologies
Technique Selection
Model Evaluation