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BABS 502. Lecture 10 March 23, 2011. Today’s Outline. Course Evaluation Contest Criterion Homework Article Discussion Case Studies Pupl Price Forecasting Long Term Care Capacity Planning Concluding Comments. Course Themes. Forecasts are necessary for effective decision making

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babs 502

BABS 502

Lecture 10

March 23, 2011

(C) Martin L. Puterman

today s outline
Today’s Outline
  • Course Evaluation
  • Contest Criterion
  • Homework Article Discussion
  • Case Studies
    • Pupl Price Forecasting
    • Long Term Care Capacity Planning
  • Concluding Comments

(C) Martin L. Puterman

course themes
Course Themes
  • Forecasts are necessary for effective decision making
    • Forecasting, planning and control are interrelated
  • Forecasts are usually wrong
    • Quantifying forecast variability is as important as determining the forecast; it is the basis for decision making.
  • Scientific methods improve forecasting

(C) Martin L. Puterman

course objectives
Course Objectives
  • To provide a structured and objective approach to forecasting
  • To provide hands on experience with several popular forecasting methods
  • To determine the data requirements for effective forecasting
  • To integrate forecasting with management decision making and planning
  • To introduce you to some advanced forecasting methods

(C) Martin L. Puterman

remember forecasting is not a statistical topic
Remember; Forecasting is NOT a Statistical Topic
  • Primary interest is not in hypothesis tests or confidence intervals.
  • Forecasts must be assessed on
    • the quality of the decisions that are produced
    • their accuracy

(C) Martin L. Puterman

forecasting considerations
Forecasting Considerations
  • Short Term vs. Medium Term vs. Long term
  • One Series vs. Many
  • Seasonal vs. Non-seasonal
  • Simple vs. Advanced
  • One-Step Ahead vs. Many Steps Ahead
  • Automatic vs. Manual
  • The role of judgment

(C) Martin L. Puterman

top 10 impediments to effective forecasting
Top 10 impediments to effective forecasting

10. Absence of a forecasting function in the organization

9. Poor data

8. Lack of software

7. Lack of technical knowledge

6. Poor data

5. Lack of trust in forecasts

4. Poor data

3. Too little time

2. Not viewed as important

1. Poor data

(C) Martin L. Puterman

scientific forecasting
Scientific Forecasting

If you’re not keeping score

you are only practicing!

(C) Martin L. Puterman

the forecasting process i
The Forecasting Process - I
  • Determine what is to be forecasted and at what frequency
  • Obtain data
  • Process the data
  • PLOT THE DATA
  • Clean the data
  • Hold out some data
    • How much?

(C) Martin L. Puterman

the forecasting process ii
The Forecasting Process - II
  • Obtain candidate forecasts
  • Assess their quality
    • Determine appropriate accuracy measures
    • Forecast accuracy on hold out data
    • Do they make sense?
    • Do they produce good decisions?
  • Revise and reassess forecasts
  • Recalibrate model on full data set
  • Produce forecasts and adjust as necessary
  • Produce report
  • In future - Evaluate accuracy of forecasts

(C) Martin L. Puterman

basic modeling concept
Basic Modeling Concept

An observed measurement

is made up of

a systematic part

and a

random part

Unfortunately we cannot observe either of these.

Forecasting methods try to isolate the systematic part.

Forecasts are based on the systematic part.

The random part determines the distribution shape and forecast accuracy.

(C) Martin L. Puterman

ten rules for data analysis
Ten rules for data analysis

Source: http://robjhyndman.com/researchtips/

Use common sense (and economic theory)

Avoid Type III errors (providing the right answer to the wrong question)

Know the context

Inspect the data

KISS (Keep It Sensibly Simple)

Make sure your results make sense

Understand the costs and benefits of data mining

Be prepared to compromise

Do not confuse statistical significance with meaningful magnitude

Always report a sensitivity analysis

techniques covered
Techniques Covered

Smoothing

Moving Averages

Lowess

Running medians

Decomposition

Exponential Smoothing

Level

Trend

Seasonal

Regression

With trend and seasonality

With explanatory variables

With lagged variables

With auto-correlated errors

ARIMA

ACFs and PACFs

Stationarity

Non-seasonal and Seasonal

Pooled methods

other techniques that can be useful in forecasting
Other techniques that can be useful in forecasting

For low counts – Poisson regression

Low demand products (sales less than 10)

“Accidents”

For binary outcomes – Logistic Regression

Success or Failures

Both these methods yield probability distributions on outcomes

Counts; P(Xt+1=k)

Binary Outcomes; P(Xt+1 = “Success”)