TM 745 Forecasting for Business

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TM 745 Forecasting for Business TechnologyDr.
Frank Joseph Matejcik
2nd Session 2/04/08 Chapter 2 The Forecast
Process, Data Considerations, and Model Selection

• South Dakota School of Mines and Technology,
  Rapid City

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Agenda New Assignment

• Chapter 2 problems 4, 8, 9
• Tentative Schedule
• About the mailing
• Chapter 2 WK (with odd diversions)
• Try to use ForecastX for Autocorrelation
• Business Forecasting 5th Edition J. Holton
  Wilson Barry KeatingMcGraw-Hill

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Tentative Schedule
Chapters Assigned 28-Jan 1 problems
1,4,8 e-mail, contact 4-Feb 2 problems 4,
8, 9 11-Feb 3 problems 1,5,8,11 18-Feb
Presidents Day 25-Feb 4 problems 6,10 3-Mar
5 problems 5,8 10-Mar Exam 1 Ch 1-4
Revised 17-Mar Break 24-Mar Easter 31-Mar
6 problems 4, 7
Chapters Assigned 7-Apr 7 3,4,5(series
A) 7B 21-Apr 8 Problem 6 28-Apr
9 05-May Final
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Web Resources

• Class Web site on the HPCnet system
• http//sdmines.sdsmt.edu/sdsmt/directory/courses/2
  008sp/tm745M021
• Streaming video http//its.sdsmt.edu/Distance/
• Answers will be online. Linked from
• The same class session that is on the DVD is on
  the stream in lower quality. http//www.flashget.c
  om/ will allow you to capture the stream more
  readily and review the lecture, anywhere you can
  get your computer to run.

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Introduction

• Outline the forecasting process
• Guide to establishment of a successful
  forecasting system
• Selection of applicable techniques
• Evaluation for trend, cyclical, seasonal
  components
• Gap, Burnett, GDP, car sales, housing starts
• Statistics review, correlogram

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The Forecast Process

• Managers that use forecasts should know how they
  were developed.
• Forecasters should know the needs of decision
  makers.
• Good communication is paramount

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The Forecast Process

• 1. Specify ObjectivesClear Statement of
  Objectives
• Discussion between "forecasters" and users
• Good communication important at this phase
• 2. Determine what to forecastSales in units or
  dollars?
• by region? by product line? export?
• Hospital admissions, discharges, patient-days,
  acuity days

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The Forecast Process

• 3. Identify the time dimensionslength how far to
  forecast? major construction or staffing
• periodicity hourly(power), daily, monthly,
  annually, etc.
• Urgency exam or next year

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The Forecast Process

• 4. Data considerations Internal or External
• Internal data periods available form(units or
  dollars)
• 5. Model Selection
• a. the pattern exhibited by the data
• b. the quantity of historic data available
• c. the length of the forecast horizon

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The Forecast Process

• 6. Model Evaluation
• Use RMSE and other measures
• Accuracy how it works in the forecast horizon
• Fit how it works in retrospectively
• WE can holdout data to check accuracy
• 7. Forecast Preparation
• Naturally, use the technique
• Use more than one with different databases

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The Forecast Process

• 8. Forecast Presentation
• Presentation is for the users
• Graphics do help
• 9. Tracking Results
• Models do deteriorate need replacement
• Forecasters can learn from mistakes

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Trend, Seasonal, and Cyclical Data Patterns

• Trend long term change in the level of the data.
  Stationary no trend (KW)
• Seasonal think seasons like summer at schools,
  resorts. Weekends, weekdays.Seasonally
  adjusted, deseasonalized
• Cyclical business cycles, inflation,
  recession
• Irregular random think wars

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Data Patterns and Model Selection

• Lets apply Table 2-1 to Figures 2-1 and through
  2-3
• POP has trend, no cycle, no seasonality
• Holts ES method
• Linear regression trend

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Data Patterns and Model Selection

• THS has trend seasonality and a cycle
• Winters ES
• Linear regression with seasonal adjustment
• Causal regression
• Time-series decomposition
• Cycle may make last two better,again

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Data Patterns and Model Selection

• DPI has a nonlinear trend, no seasonality and no
  cycle
• Nonlinear regression
• Causal regression
• Holts ES
• When you finish the text you willbe able to make
  good forecastsfor a wide variety of patterns.

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A Statistical Review

• Read it as fast as you should. Quickly for many.
• Confidence level is usually used for
  intervalsSignificance level for tests.
• We will go through the book review slides
  quickly, if there is interest we will slow it
  down.
• Well do a little on the correlation coefficient,
  my weird way.

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Katyas Triangle and the correlation coefficient r
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Anscombe Appropriate
Look at horizontal / long side in the central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
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Anscombe Appropriate
Look at horizontal / long side in the central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
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Anscombe Outlier
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
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Anscombe Outlier
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
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Anscombe Unbalanced
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
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Anscombe Unbalanced
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Anscombe Unbalanced
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Anscombe Parabola
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Anscombe Parabola
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R-.62
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R.41
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R -.95
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Correlograms An Alternative Method of Data
Exploration
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Disposable Personal Income Exploratory Data
Analysis Model Selection pics after

• Monthly data, so more variable than Figure 2.6.

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Total Houses Sold Exploratory Data Analysis
Model Selection pics after

• Monthly data seasonality, trend
• Winters exponential smoothing
• Regression with trend and seasonality
• Causal regression
• Time series decomposition
• ARIMA

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Integrative Case The Gap

• Should we talk through it with scans or WK slides?

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Using ForecastX to Find Autocorrelation Functions

• Lets see if we can get it to work