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

Chapter Topics Data Patterns Forecasting Methodologies Technique Selection Model Evaluation

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.

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.

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.

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

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

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

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

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.

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.

Error in Forecasting Mean Average Deviation (MAD)

Error in Forecasting Mean Square Error (MSE)

Error in Forecasting Mean Absolute Percentage Error

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

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

Evaluating Reliability Root Percent Mean Square (R%MS)

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

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.

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

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

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

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)

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

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.

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.

Model Evaluation

Model Evaluation

Model Evaluation Figure 2.7 Turning Point Analysis for Model C

Chapter Summary Data Patterns Forecasting Methodologies Technique Selection Model Evaluation