Quantitative forecasting methods in management

Prof. Dr. Algirdas Budrevicius

Vilnius University, Faculty of Communication

Course website: http://www.kf.vu.lt/~albud/progn/Portugal

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Famous forecasting quates--William Shakespeare:

"If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me. "

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• "It is far better to foresee even without certainty than not to foresee at all. "

• --Henri Poincare in The Foundations of Science, page 129.

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Course materials

• Course description: Website http://www.kf.vu.lt/~albud/progn/Portugal

• Demonstrations: PowerPoint presentations

• Time-series data, forecasting examples (Excel workbooks), assignments

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Course plan

• Look: http://www.kf.vu.lt/~albud/progn/Portugal#Lectures

Part 1. Forecasting methods in general

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History: development of the forecasting technique

• Non scientiffic forecasting: e.g. Astrology, Book of Changes.

• 19-20 century. Demographic forecasts

• Development of the quantitative methods: middle-to-second part of the 20th century.

• New developments: Neural network based methods

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Current situation in forecasting

• Forecasting is widely used in management now

• There exist a well defined set of quantitative forecasting methods

• There exists computer software that may be quite simply applied in forecasting

• Excel program allows to solve simple forecasting tasks

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Forecasting in management

• Personnel management

• Resource management

• Finance management

• Organizational management

Forecasting is usedin various domains of management:

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Taxonomy of forecasting methods

• Methods: quantitative and qualitative• Qualitative: judgmental (based on expert

opinions) and technological (used for long term forecasting)

• Quantitative: time series methods and reasoning

• Note: only time series methods will be considered in this course.

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Naive forecasting techniques

• Naive forecasts: a “folk forecasting technique”

• In every day life situations we forecast using very simple technique

• This technique is close to linear trend model

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Naive forecasts NF1 and NF2

• NF1. (“The value tomorow will be the same as today”). Example: Number of visitors today was 120. Forecast NF1 for tomorow: 120.

• NF2. (“The value tomorow will be less (greater) by …10% ”). Example: Average temperature this month is 20 degrees. Forecast NF2 for the next month: Temperature will be 25 degrees (increase of 25%).

Quantitative time-series based forecasting: a rationale

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Time-series methods of forecasting

• Time-series analysis relies on historical data and attempts to project historical patterns into the future

• Note: only time series methods will further be considered

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Example of time-series data

Number of visitors (in th.)

Year 1998 1999 2000 2001 2002 2003

Number 420 450 440 460 470 465

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Example of time-series data

EVOLUÇÃO DA CAPACIDADE DOS ESTABELECIMENTOS HOTELEIROS EM PORTUGAL (a) (Camas) Anos Total Geral Hotéis Pousadas e Estalagens Pensões Hotéis – Apartamentos

Motéis 1992 151063 76427 4035 44845 24344 14121993 155356 77199 4452 46372 26079 12541994 161729 82353 4767 43327 30025 12571995 162954 83372 4873 43306 30166 12371996 166030 86598 5081 42732 30383 12361997 164066 88601 5716 41979 26308 14621998 168396 93357 6186 40310 27013 15301999 170863 94217 6446 40537 28076 15872000 177328 98434 6826 40721 29764 15832001 183919 101684 7112 41588 31757 17782002 187265 104727 7460 40594 32725 17592003 192480 109528 7442 41930 31755 1825

Fonte: INE (a) – Não inclui Aldeamentos nem Apartamentos Turísticos Nota: Dados de 2001, 2002 e nćo comparįveis com as séries anteriores, face ą nova metodologia utilizada pelo INE. http://www.dgturismo.pt/WebAttachment%5CCapacidade%20de%20Alojamento%201993-2003.pdf

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Definition of a forecasting situation

• Data (time series, or historical data)

• Forecasting method (e.g. Moving average, Trend analysis)

• Forecast

• Error of forecast

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Basic steps of a forecasting task

• Defining the problem• Choosing time-series data• Analysing visually data paterns • Choosing a model• Calculating a forecast• Evaluating the forecasting accuracy, caculating

errors

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Recomended form to present data and forecasts: an example

Year Number of visitors

Forecast Error

1995

1996

...

2006 (forecast)

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Example of dataNework of Municipal Public Libraries in Lithuania in 1991-2002

Year Number of libraries 1991 16621992 15691993 15211994 15141995 15061996 14841997 14731998 14591999 14472000 14482001 14272002 1400

Source: Statistics of Lithuanian Libraries.

Municipal public libraries in Lithuania in 1991-2002

13001400150016001700

19901991199219931994199519961997199819992000200120022003

Year

Nu

mb

er

of

lib

rari

es

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Patterns of the time-series data

A forecasting method should comply with the data pattern.

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Example of forecastingForecasting using linear trend. Demonstration

1. Calculating correlation: 0,995741Week Number of library visitors Signifficant correlation

1 1063 2. Plotting a chart (XY scatter)2 2369 3. Adding a linear trend line3 3159 Options: display equation4 3964 4. Calculating the forecast 5 5001 (by inserting number of the week x=6 into the equation)

6 5. Evaluation (using RSQ) 0,99Very good fitting

Forecasted number of visitors: 5953

Number of library visitors

y = 947,1x + 269,9

R2 = 0,9915

0

2000

4000

6000

8000

0 1 2 3 4 5

Week

Vis

ito

rs

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Forecast accuracy

• Forecast accuracy can be determined only when actual (future) data are available.

• Error of a forecast is measured as a difference between the actual and forecasted values

End