welcome to mm305 unit 5 seminar prof greg forecasting
TRANSCRIPT
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Welcome to MM305
Unit 5 Seminar
Prof Greg
Forecasting
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What is forecasting?What is forecasting?
• An attempt to predict the future using data.
• Generally an 8-step process1. Why are you forecasting?2. What are you forecasting?3. When are you forecasting to?4. How you are going to forecast.5. Gather the needed data6. Validate your forecasting model7. Make the forecast8. Implement the results (make use of it)
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Regression Analysis
Multiple Regression
MovingAverage
Exponential Smoothing
Trend Projections
Decomposition
Forecasting ModelsForecasting Models
Figure 5.1
Delphi Methods
Jury of Executive Opinion
Sales ForceComposite
Consumer Market Survey
Time-Series Methods
Qualitative Models
Causal Methods
Forecasting Techniques
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Forecasting MethodsForecasting Methods Qualitative
Qualitative models incorporate judgmental or subjective factor Useful when subjective factors are thought to be
important or when accurate quantitative data is difficult to obtain
Time Series Time-series models attempt to predict the
future based on the past
Causal Models Causal models use variables or factors that
might influence the quantity being forecasted
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Components of a Time Series
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Measures of Forecast AccuracyMeasures of Forecast Accuracy
• Mean Absolute Deviation (MAD):MAD = |forecast error| / T
= |At - Ft| / T
• Mean Squared Error (MSE):MSE = (forecast error)2 / T
= (At – Ft)2 / T
• Mean Absolute Percent Error (MAPE):
MAPE = 100 (|At - Ft|/ At) / T
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General Forms of Time-Series ModelsGeneral Forms of Time-Series Models
There are two general forms of time-series models:
• Most widely used is multiplicative model, which assumes forecasted value is product of four components.
Forecast = (Trend) *(Seasonality) *(Cycles) *( Random)
• Additive model adds components together to provide an
estimate.
Forecast = Trend + Seasonality + Cycles + Random
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Causal ModelsCausal Models
• Goal of causal forecasting model is to develop best statistical relationship between dependent variable and independent variables.
• Most common model used in practice is regression analysis.
• In causal forecasting models, when one tries to predict a dependent variable using:• a single independent variable -simple regression model • more than one independent variable -multiple
regression model
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Trend Projection
• Fits a trend line to a series of historical data points
• The line is projected into the future for medium- to long-range forecasts
• Several trend equations can be developed based on exponential or quadratic models
• The simplest is a linear model developed using regression analysis
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Seasonal VariationsSeasonal Variations
• Recurring variations over time may indicate the need for seasonal adjustments in the trend line
• A seasonal index indicates how a particular season compares with an average season
• When no trend is present, the seasonal index can be found by dividing the average value for a particular season by the average of all the data
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Moving Average (MA)Moving Average (MA)
• MA is a series of arithmetic means
• Used if little or no trend
• Used often for smoothing• Provides overall impression of data over time
• Equation:
MA = (Actual value in previous k periods) / k
n
YYYF ntttt
111
...
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Excel QM: 3-Year Moving Average (Page 172)
Excel QM: 3-Year Moving Average (Page 172)
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Weighted Moving Averages (WMA)Weighted Moving Averages (WMA)
• Used when trend is present • Older data usually less important
• Weights based on intuition• Equation:
WMA = (weight for period i) (actual value in period i)
(weights)
n
ntnttt www
YwYwYwF
...
...
21
11211
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Excel QM Excel QM
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Exponential Smoothing (ES)Exponential Smoothing (ES)• A form of weighted moving average
• Weights decline exponentially• Most recent data weighted most
• Requires smoothing constant ()• ranges from 0 to 1• is subjectively chosen
• Equation:
Ft= Ft-1 + ( At-1 - Ft-1 )
)- tperiodfor forecast - - tperiodin value(actual - tperiodfor forecast
t periodfor Forecast
111
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Selecting the Smoothing Constant Selecting the Smoothing Constant • Selecting the appropriate value for is key to
obtaining a good forecast
• The objective is always to generate an accurate forecast
• The general approach is to develop trial forecasts with different values of and select the that results in the lowest MAD
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Excel QM: Port of Baltimore Example (page 177)
Excel QM: Port of Baltimore Example (page 177)
Program 5.2B
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Time-Series Forecasting ModelsTime-Series Forecasting Models
• A time series is a sequence of evenly spaced events
• Time-series forecasts predict the future based solely of the past values of the variable
• Other variables are ignored
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Decomposition of a Time SeriesDecomposition of a Time Series
• Trend (T) -- upward and downward movement
• Seasonality (S) -- Demand fluctuations
• Cycles (C) -- Patterns in annual data
• Random Variation s (R) – “Blips” caused by
chance
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Trend Projection Trend projection fits a trend line to a
series of historical data points
The line is projected into the future for medium- to long-range forecasts
The simplest is a linear model developed using regression analysis Ŷ = b0 +b1X
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Excel QM—Regression/Trend AnalysisExcel QM—Regression/Trend Analysis
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Midwestern Manufacturing Company ExampleMidwestern Manufacturing Company Example
The forecast equation is
To project demand for 2008, we use the coding system to define X = 8
Likewise for X = 9
XY 54107156 ..ˆ
(sales in 2008) = 56.71 + 10.54(8)= 141.03, or 141 generators
(sales in 2009) = 56.71 + 10.54(9)= 151.57, or 152 generators
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Month Ending
Rating 3-mo Moving Average Weighted 3-mo Moving Average (1st mo=3, 2nd mo=2, 3rd mo=1)
3-mo Absolute Deviation
3-mo Weighted Absolute Deviation
1 2.0
2 2.2
3 2.5
4 1.9 (2.0+2.2+2.5)/3 = 2.23 (6.0+4.4+2.5)/6 = 2.15 0.33 0.25
5 2.3 (2.2+2.5+1.9)/3 = 2.20 (6.6+5.0+1.9)/6 = 2.25 0.10 0.05
6 2.0 (2.5+1.9+2.3)/3 = 2.23 (7.5+3.8+2.3)/6 = 2.27 0.23 0.27
Total Deviations 0.66 0.57
MAD 0.22 0.19
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Hour Lottery Ticket Sales
8 am 150
10 am 142
12 noon 190
2 pm 223
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