1 / 31

Reminders

Reminders. HW 3 Posted HW 1 Graded and Posted Grading appeal process. MGTSC 352. Lecture 6: Forecasting Wrap-up of Forecasting Holdout strategy Debugging Forecasting Models Monte Carlo Simulation Playing Roulette with Excel Bard Outside example. 95% Prediction Interval.

mircea
Download Presentation

Reminders

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Reminders • HW 3 Posted • HW 1 Graded and Posted • Grading appeal process

  2. MGTSC 352 Lecture 6: Forecasting Wrap-up of ForecastingHoldout strategyDebugging Forecasting Models Monte Carlo SimulationPlaying Roulette with ExcelBard Outside example

  3. 95% Prediction Interval • Technically correct formula; • Forecast + Bias + 2 x Std Error • Heuristic for use in this class; • Forecast  2  SE

  4. Pg. 39 Steps in a Forecasting Project -1: Collect data 0: Plot the data (helps detect patterns) 1: Decide which models to use • level – SA, SMA, WMA, ES • level + trend – SLR, DES • level + trend + seas. – TES, SLR w SI, ... 2: Use models 3: Compare and select (one or more) 4: Generate forecast and range (prediction interval) More on selection

  5. Appropriate model... Nonlinear (ex. power) linear S-curve (ex. any CDF)

  6. Calgary EMS Data Number of calls / month Trend? Seasonality?

  7. Checking for (Yearly) Seasonality Number of calls / month

  8. Weekly or Hourly Seasonality Avg. # of calls / hr., 2004

  9. Pg. 41 How to select a model? • Look at performance measures • BIAS, MAD, MAPE, MSE • Use holdout strategy • Example: 4 years of data • Use first 3 years to fit model(s) • Forecast for Year 4 and check the fit(s) • Select model(s) • Refit model(s) adding Year 4 data • If you have more than one good model... COMBINE FORECASTS

  10. Example: Building Materials, Garden Equipment, and Supply Dealers

  11. TES vs. SLR w SI(Both optimized to minimize SE) Which method would you choose?

  12. One possibility: Combining Forecasts TES SLR w SI weight + (1 - weight) Minimize SE of the combined forecast to find the best weight

  13. Holdout Strategy • Ignore part of the data (the “holdout data”) • Build models using the rest of the data • Optimize parameters • Forecast for the holdout data • Calculate perf. measures for holdout data • Choose model that performs best on holdout data • Refit parameters of best model, using all data

  14. holdoutperiod TES vs. SLR w/ SI…in holdout period

  15. TES vs. SLR w SI …… in holdout period Now which method would you choose?

  16. Holdout Strategy Recap • Performance during holdout period: a.k.a. “out of sample” performance • In other words: how well does the method perform when forecasting data it hasn’t “seen” yet? • Question: Why is SE during holdout period worse than SE during “training period”?

  17. Do we have to implement these models from scratch? • Forecasting software survey • http://lionhrtpub.com/orms/surveys/FSS/FSS.html • General statistics program • Minitab, NCSS, SAS, Systat • Dedicated forecast software • AutoBox, Forecast Pro (MGTSC 405)

  18. Do Spreadsheet Models Have Errors? • Field audits of real-world spreadsheets: 94% had errorshttp://panko.cba.hawaii.edu/ssr/Mypapers/whatknow.htm • What are the consequences of spreadsheet errors? • Incorrect financial statements • Bad publicity, loss of investor confidence • Lawsuits • Loss of election • See http://www.eusprig.org/stories.htm for more

  19. Debugging – Finding Your Mistakes • Before entering a formula: • Pause and predict the result • After entering a formula: • Double-click to see where numbers are coming from • Try simple test values: 0, 1 • Graph your results • ctrl+~ – use to look for breaks in patterns To Excel

  20. Playing roulette with Excel To Excel …

  21. Game 1 • Spin the spinner once • Payoff = (spinner outcome)  ($1 Million) • Q1: What would you pay to play this game? • Q2: Suppose the game were played 10,000 times. What do you think the payoff distribution will look like?

  22. Game 2 • Spin the spinner twice • Payoff = ($1 Million) x (spinner outcome 1 + spinner outcome 2)/2 Q1: What would you pay to play this game? • Q2: Suppose the game were played 10,000 times. What do you think the payoff distribution will look like?

  23. Game 1 payoff distribution

  24. Game 2 payoff distribution

  25. Using Excel to get the right answer • Simulate one spin: =RAND() • Repeat 10,000 times • Plot histogram • To Excel

  26. Pg. 43 Excel Details • Using Data tables to replicate a simulation • Enter replication numbers (1, …, n) in leftmost column • Enter formulas for outputs in top row • Highlight table • Data  Table … • Column input cell: any empty cell

  27. “Freezing” simulated values: Copy the values Paste special …  values Frequency distributions:(see also pg. 134) Generate sample Enter “bins” values Highlight range where frequencies should be calculated =FREQUENCY(sample, bins) “Ctrl + shift + enter” instead of just “enter.” More Excel Details

  28. Bard Outside • The Bard Outside theatre group puts on plays by Shakespeare 20 times every summer in a 200-seat outdoor theatre. • Data: • Attendance and weather (rain / no rain) for last five seasons (5 x 20 = 100 shows) • Revenue = $10 per customer • Cost = $1,600 per show • Question: how much would profit increase if the number of seats were increased?

  29. Data Analysis • What’s the probability of rain? • What is the mean and standard deviation of demand when it rains? • How about when it doesn’t rain? • How can we simulate demand? To Excel …

  30. Simulating Profit per show • Simulate weather • Simulate demand • Make sure 0 ≤ demand ≤ capacity • Calculate revenue • Subtract cost • Replicate! • Remember: freeze tables of simulation results

  31. Final results

More Related