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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.

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Presentation Transcript
reminders
Reminders
  • HW 3 Posted
  • HW 1 Graded and Posted
  • Grading appeal process
mgtsc 352

MGTSC 352

Lecture 6: Forecasting

Wrap-up of ForecastingHoldout strategyDebugging Forecasting Models

Monte Carlo SimulationPlaying Roulette with ExcelBard Outside example

95 prediction interval
95% Prediction Interval
  • Technically correct formula;
    • Forecast + Bias + 2 x Std Error
  • Heuristic for use in this class;
    • Forecast  2  SE
steps in a forecasting project

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

appropriate model
Appropriate model...

Nonlinear (ex. power)

linear

S-curve (ex. any CDF)

calgary ems data
Calgary EMS Data

Number of calls / month

Trend?

Seasonality?

checking for yearly seasonality
Checking for (Yearly) Seasonality

Number of calls / month

weekly or hourly seasonality
Weekly or Hourly Seasonality

Avg. # of calls / hr., 2004

how to select a model

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

one possibility combining forecasts
One possibility: Combining Forecasts

TES

SLR w SI

weight

+ (1 - weight)

Minimize SE of the combined forecast to find the best weight

holdout strategy
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
tes vs slr w si in holdout period1
TES vs. SLR w SI …… in holdout period

Now which method would you choose?

holdout strategy recap
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”?
do we have to implement these models from scratch
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)
do spreadsheet models have errors
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
debugging finding your mistakes
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

game 1
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?
game 2
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?
using excel to get the right answer
Using Excel to get the right answer
  • Simulate one spin: =RAND()
  • Repeat 10,000 times
  • Plot histogram
        • To Excel
excel details

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
more excel details
“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
bard outside
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?
data analysis
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 …

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