Business Forecasting

Chapter 2Data 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 When there is no trend in the data pattern,

we deal with horizontal data pattern.

Mean

Time

Fore

cast

V

ari

able

Data Patterns

Trend Long-term growth movement of a time

series

t t

tt

YtYt

Yt Yt

Trend Trend

Trend

Trend

Data Patterns Seasonal Pattern

A predictable and repetitive movement observed around a trend line within a period of 1 year or less.

Time

Fore

cast

Vari

able

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.

ttt YYe ˆ

Error in Forecasting

Mean Average Deviation (MAD)

n

en

tt

1MAD

Error in Forecasting

Mean Square Error (MSE)

n

en

tt

1

2 )(

MSE

Error in Forecasting

Mean Absolute Percentage Error

n

t

tt

n

Ye

1

100)/(MAPE

Error in Forecasting

Mean Percentage Error

No bias, MPE should be zero.

n

Yen

ttt

1

)/(

MPE

Evaluating Reliability

Forecasters use the following two

approaches to determine if the forecast

is reliable or not: Root Mean Square (RMS)

n

e

RMS

n

tt

1

2

Evaluating Reliability

Root Percent Mean Square (R%MS)

n

Ye

MSR

n

ttt

1

2 )/(

%

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

12101 ttt YYY 1tY

tY

1tY

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

-25-20-15-10-505

1015202530

Time

Cu

mu

lati

ve E

rror Model A

Model B

Model C

Model D

Model Evaluation

Actual Change Y Line of Perfect Forecast II–Turning Point Error IB–Underestimate Prediction of downturn of Positive that did not occur; or change failure to predict an IA–Overestimate upturn of Positive change

IIIA–Overestimate of Y Forecast Negative change Change IV–Turning Point Error Prediction or upturn that did not occur; or failure to predict a downturn IIIB–Underestimate of Negative change Figure 2.6 Turning Point Error Diagram

Model Evaluation

-400

-300

-200

-100

0

100

200

300

400

-300 -200 -100 0 100 200 300 400

Actual Change

Fore

cast

Cha

nge

Figure 2.7 Turning Point Analysis for Model C

Chapter Summary

Data Patterns

Forecasting Methodologies

Technique Selection

Model Evaluation