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TM 745 Forecasting for Business

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Gap, Burnett, GDP, car sales, housing starts. Statistics review, correlogram ... Disposable Personal Income: Exploratory Data Analysis & Model Selection pics after ... – PowerPoint PPT presentation

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Title: TM 745 Forecasting for Business


1
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

2
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

3
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
4
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.

5
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

6
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

7
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

8
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

9
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

12
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

13
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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17
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

18
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

19
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.

20
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.
41
Anscombe Appropriate
Look at horizontal / long side in the central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
42
Anscombe Outlier
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
43
Anscombe Outlier
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
44
Anscombe Unbalanced
Look at horizontal / long side in The central
triangle (Katyas) to get r. r has the same
sign as the scatter slope.
45
Anscombe Unbalanced
46
Anscombe Unbalanced
47
Anscombe Parabola
48
Anscombe Parabola
49
R-.62
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R.41
51
R -.95
52
Correlograms An Alternative Method of Data
Exploration
53
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?

60
Using ForecastX to Find Autocorrelation Functions
  • Lets see if we can get it to work
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