Introduction to Forecasting Techniques

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FORECASTING TECHNIQUES (DQQ 8043)

NOR IDAYU MAHAT

Department of Physical Science (Statistics)

College of Art and Sciences, Universiti Utara Malaysia

Tel: 04-928 4098, e-mail: [email protected]

FORECASTING TECHNIQUES (DQQ 8043) p. 1/


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Course Summar

Objectives: to learn some statistical methods for forecasting

purposes and to understand the concepts and ideas used inforecasting.

Evaluation: Coursework 70%

Individual test (10%): 1 hour multiple choice test Group assignment I (30%): report writing Group assignment II (30%): presentation

Final exam 30%2 hours open book examination.



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Course Summar

References Text book: Bowerman, B. L., OConnell, R.T. and

Koehler, A.B. (2005). Forecasting, Time Series and Regression . California: Thomson.

Other references as suggested in the syllabus or anyrelevant books.

Software: any relevant software but Excel and SPSS arepreferable.



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Introduction to Forecastin

What does forecasting mean?

Forecast - predictions of future events or conditions. Forecasting - activity of making such predictions. Forecasting techniques have been widely used in many

areas: demand in consumer preferences, production

scheduling, strategic management, weather forecast, sportforecast etc.



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Introduction to Forecastin

What does forecasting mean?

Forecast - predictions of future events or conditions. Forecasting - activity of making such predictions. Forecasting techniques have been widely used in many

areas: demand in consumer preferences, production

scheduling, strategic management, weather forecast, sportforecast etc.

Forecasting strategy:

1. Identify a pattern based on the existing data.2. Choose a suitable method to model the pattern.3. Measure the performance of the constructed model.4. If it is accepted and the pattern is believed to be

continued in the future, then use the model for predictionpurpose.



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Basic Consideration to Successful Forecastin

What are the implications of the forecasting modeling in thedecision environment?

What is the object that we want to forecast? How do we wish to state our forecasts? What is thestatement of our forecast?

What is the forecast horizon? Are the available data simply the past history of the series

to be forecast? What forecasting method is best suited to the needs of a

particular forecasting problem?



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Basic Understanding about Forecastin

Types of data cross-sectional

time series functional area/geography and many more



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Basic Understanding about Forecastin

Types of data cross-sectional

time series functional area/geography and many more

Factors that must be considered in choosing appropriateforecasting method

Time frame

The pattern of data The cost (collecting the data, performing the analysis,forecasting etc.)

The accuracy desired The ease of operation and understanding The demand of customer and stakeholder



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Forecasting Method

Basic types: qualitative methods and quantitativemethods .



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Forecasting Method


Qualitative methods Decision is made based on knowledge and experiences

of experts. Subjective decision making.



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Forecasting Method


Qualitative methods Decision is made based on knowledge and experiences

of experts. Subjective decision making.

Quantitative methods Decision is made based on the results obtained from the

analysis of the historical data.



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Qualitative Metho

Some common methods1. Subjective curve tting (e.g. product life cycle,

exponential curve).2. Delphi method: opinion based on a panel of experts.3. Time-independent technological comparisons.

These techniques are sensible to use when unavailable of historical data, or decision is made in non-critical problem.

Some weaknesses of qualitative methods All predictions are subjective. Difcult to measure how good/reliable the decision that

has been made for future use. Different experts may give different opinions.FORECASTING TECHNIQUES (DQQ 8043) p. 8/


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Quantitative Method

These methods can be classied into mathematical andalgorithm approaches.

Quantitative methods depend on Number of variables. Types of variables.

Size of sample data. Types of forecast: point forecast or interval forecast.

Forecasting contains errors! Its occur because forecasting involve some degrees of

uncertainty. Errors may be caused by irregular components,

unpredictable events or researcher mistake. Size of error must be as small as possible.



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Some Issues in Formulating Quantitative Mode

Accuracy of the forecast using the constructed model. Forecast horizon as it can inuence the decision making.

Changes in technologies may effect the signicant offorecast.

Availability of information/data.

Others surrounding inuences: Control from authorize bodies or the government Unpredictable changes: e.g. natural disaster Cost



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Factors that Contribute to False Forecas

There can be many factors that contribute to false forecast orprediction. However most of these are due to

Do not understand the problem!! Using inappropriate data for achieving the determined

objective(s).

Implementing wrong models. Applying wrong assumptions. Using bias models for prediction purpose:

Using complex methods with insufcient knowledgeabout them. Performing wrong statistical testing. Including unnecessary variables in the constructed

models. Using messy data.



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Forecasting Procedur

1. Identication and determination of the purpose and theobjective of the forecasting study

2. Selecting of relevant theory3. Collection of data (primary of secondary data)4. Initial analysis on the data

5. Model construction6. Assessing the constructed model7. Forecast

8. Documentation



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Reviewing on Some Statistical Concept

Objects and variables



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Objects and variables Population

The entire collection of objects or observations aboutwhich information is desired. Number of observations in population may nite or

innite.

Values obtain from population are called parameters e.g.mean, standard deviation, range, maximum andminimum values etc.



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Objects and variables Population

The entire collection of objects or observations aboutwhich information is desired. Number of observations in population may nite or

innite.

Values obtain from population are called parameters e.g.mean, standard deviation, range, maximum andminimum values etc.

Sample A sample contains some selected objects or

observations from the target population. We often use sample when values of parameters that we

are interested into are unavailable (unknown). To avoid bias in estimation, random sample should be

used. FORECASTING TECHNIQUES (DQQ 8043) p. 13/


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Initial Data Analysi

Aims: to uncover any patterns that may be present in the data. to understand the distribution of the data. to identify any anomalies, if exist.

The best way to discover pattern in the data is through

visualisation. Sometimes, table (tabular) may be appropriate.

Simple statistics can be used such as the location values,the variation values and the linear association values. Ifappropriate, inferential analysis can be done.



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Example: New York Air Qualit

Wind

60 70 80 90

5

1 0

1 5

2 0

6 0

7 0

8 0

9 0

Temp

5 10 15 20 5.0 6.0 7.0 8.0

5 . 0

6 . 0

7 . 0

8 . 0

Month



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Example: Iri

Sepal.Length

2.0 3.0 4.0 0.5 1.5 2.5

4 . 5

5 . 5

6 . 5

7 . 5

2 . 0

3 . 0

4 . 0

Sepal.Width

Petal.Length

1

2

3

4

5

6

7

0 . 5

1 . 5

2 . 5

Petal.Width



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Inferential Statistic

Hypothesis testing that evaluates the correctness of the estimated value tests for the existence of relationship between variables compares two or more populations many more . . .

Modelling to understand behaviour between variables or objects. to uncover relationship between variables so that some

predictions for future use can be made.



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Example: Quality Home Improvement Center (QHIC

QHIC operates ve stores in a large metropolitan area. Themarketing department at QHIC wishes to study the relationshipbetween home value (in thousand $), x, and yearlyexpenditure on home upkeep ($) , y. A random sample of 40homeowners is taken, and they are asked to estimate theirexpenditures during the previous year on the types of homeupkeep products and services offered by QHIC.

Some information of this sample: Average home value: $ 174.49 Average yearly to upkeep the house: $ 918.09 Pattern of relationship between two variables: straight line /

linear



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Model Construction

Some existing statistical forecasting techniques. Regression analyses (simple, nonlinear or multiple). Regression time series. Classical decomposition. Exponential smoothing. Box-Jenkins methods.

Other methods Non-parametric time series.

Robust time series.



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Model Assessmen

An important process to measure the performance of the constructed model. check the validity of the constructed model.

The indicator can be different, depends on the aim of thestudy. However, in general, error is used.



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Model Assessmen



Error can be measured as a different between the actual and thepredicted values, et = yt yt , where t = 1 , . . . , n .



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Model Assessmen



Error can be measured as a different between the actual and thepredicted values, et = yt yt , where t = 1 , . . . , n .

Actual value ( yt ) is a value collected from the actual event. Predicted value ( yt ) is a value estimated (obtained) from the

constructed model.



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Some Indicators to Measure Erro

Let yt be the actual value from the obtained data and itsassociated predicted value be labeled as yt . Then, the forecasterrors might be measured using

1. Sum of errors:n

t =1(yt yt )

2. Sum of squared of errors:

n

t =1 (yt yt )2

3. Mean absolute deviation of error:n

t =1|yt yt | /n

4. Mean squared of error:n

t =1(yt yt )2 /n


Introduction to Forecasting Techniques

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