Reminders

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# Reminders - PowerPoint PPT Presentation

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
• HW 3 Posted
• HW 1 Graded and Posted

### MGTSC 352

Lecture 6: Forecasting

Wrap-up of ForecastingHoldout strategyDebugging Forecasting Models

Monte Carlo SimulationPlaying Roulette with ExcelBard Outside example

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

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...

Nonlinear (ex. power)

linear

S-curve (ex. any CDF)

Calgary EMS Data

Number of calls / month

Trend?

Seasonality?

Checking for (Yearly) Seasonality

Number of calls / month

Weekly or Hourly Seasonality

Avg. # of calls / hr., 2004

Pg. 41

How to select a model?
• Look at performance measures
• 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

TES

SLR w SI

weight

+ (1 - weight)

Minimize SE of the combined forecast to find the best weight

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 period

Now which method would you choose?

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?
• 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)
• 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
• 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
• ctrl+~ – use to look for breaks in patterns

To Excel

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
• 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
• Simulate one spin: =RAND()
• Repeat 10,000 times
• Plot histogram
• To Excel

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
“Freezing” simulated values:

Copy the values

Paste special …  values

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
• 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
• 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
• Simulate weather
• Simulate demand
• Make sure 0 ≤ demand ≤ capacity
• Calculate revenue
• Subtract cost
• Replicate!
• Remember: freeze tables of simulation results