Forecasting
Professor Ahmadi
Slide 1
Learning Objectives
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Understand when to use various types of forecasting
models and the time horizon.
Understand qualitative and quantitative tools of
forecasting.
Compute moving averages and exponential smoothing
models.
Analyze trends and seasonality in time-series data.
Compute variety of forecasting error measures.
Use variables in linear regression model.
Use Excel to analyze variety of forecasting models
Slide 2
Eight Steps to Forecasting
1. Determine use of forecast - what objective are we
trying to obtain?
2. Select items or quantities to be forecasted.
3. Determine time horizon of forecast:
• Short-range (less than three months)
• Medium-range (3 months to 3 years)
• Long-range (3+ years)
4. Select forecasting model or models.
5. Gather data needed to make forecast.
6. Validate forecasting model.
7. Make forecast.
8. Implement results.
Slide 3
Types of Forecasts
Slide 4
Qualitative Models
• Qualitative models attempt to incorporate judgmental or
subjective factors into forecasting model.
• Opinions by experts, individual experiences and
judgments, and other subjective factors may be
considered.
• Qualitative models are especially useful when
subjective factors are expected to be very important or
when accurate quantitative data are difficult to obtain.
• Qualitative models are also useful for long-term
forecasting.
Slide 5
Measuring Forecast Error
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Mean Absolute Deviation (MAD):
• MAD =  |forecast error| / T =  |At - Ft| / T
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Mean Squared Error (MSE):
• MSE =  (forecast error)2 / T =  (At – Ft)2 / T
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Mean Absolute Percent Error (MAPE):
• MAPE = 100  (|At - Ft|/ At) / T
Slide 6
Time - Series and Causal (Associative) Models
1. Time-series Models:
• Time-series models attempt to predict future by using
historical data.
• Models make assumption that what happens in future
is a function of what has happened in past.
2. Causal Models:
• As with time-series models, causal models also rely on
quantitative data.
• Bivariate and multivariate regression models are
examples of associative models.
Slide 7
Moving Average
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MA is a series of arithmetic means
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Used if little or no trend
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Used often for smoothing
• Provides overall impression of data over time
Equation:
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MA = (Actual value in previous k periods) / k
Slide 8
Weighted Moving Averages
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Used when trend is present
• Older data usually less important
Weights based on intuition
Equation:
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k-period weighted moving average =
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 (weight for period i) (actual value in period i)
 (weights)
Slide 9
Exponential Smoothing
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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 )
Slide 10
Time series Components
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The four Components of Time Series are:
1. Trend
2. Seasonal
3. Cyclical
4. Random
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The time series can be decomposed into its four
components.
Slide 11
General Forms of Time-series Models
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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 is also available. It is stated as:
Forecast = Trend + Seasonality + Cycles + Random
Slide 12
Causal Models
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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 dependent variable using single
independent variable it is called a simple regression
model.
When one uses more than one independent
variable to forecast dependent variable, it is called
a multiple regression model.
Slide 13