1 BABS 502 Lecture 10 March 23, 2011 (C) Martin L. Puterman

2 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

3 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

4 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

5 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

6 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

7 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

8 Scientific Forecasting If you’re not keeping score you are only practicing! (C) Martin L. Puterman

9 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

10 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

11 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 Source: 1.Use common sense (and economic theory) 2.Avoid Type III errors (providing the right answer to the wrong question) 3.Know the context 4.Inspect the data 5.KISS (Keep It Sensibly Simple) 6.Make sure your results make sense 7.Understand the costs and benefits of data mining 8.Be prepared to compromise 9.Do not confuse statistical significance with meaningful magnitude 10.Always report a sensitivity analysis

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 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(X t+1 =k) –Binary Outcomes; P(X t+1 = “Success”)

Tomorrow belongs to people who prepare for it today African Proverb