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Unformatted text preview: (hopefully) Objective Forecasting Methods
Two primary methods: causal models and time series
Let Y be the quantity to be forecasted and (X1,
X2, . . . , Xn) be n variables that have predictive power
for Y. A causal model is
Y = f (X1, X2, . . . , Xn).
A typical relationship is a linear one. That is,
Y = a0 + a1X1 + . . . + an Xn. 4 Time Series Methods
A time series is just collection of past values of the
variable being predicted. Also known as naive methods.
Goal is to isolate patterns in past data.
Randomness Time Series Patterns 5 Notation Conventions
Let D1, D2, . . . Dn, . . . be the past values of the series to
be predicted (observed demand). If we are making a
forecast in period t, assume we have observed Dt , Dt-1
Let Ftt, t + τ be forecast made in period t for the demand in
period t + τ where τ = 1, 2, 3, …
Then Ft -1, t is the forecast made in t-1 for t and
Then 1, is
iis the forecast made in t for t+1. (one step ahead) Use
shorthand notation Ft = Ft - 1, t .
shorthand Evaluation of Forecasts
The forecast error in period t, et, iis the difference
between the forecast for demand in period t and the
actual value of demand in t.
For a multiple step ahead forecast:
et = Ft - τ, t -Dt.
For one step ahead forecast: et = Ft - Dt.
MAD = (1/n) Σ | ei |
MSE = (1/n) Σ ei 2 6 Example
Compare the accuracy of the forecasts of two managers
of a SRAM manufac...
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This document was uploaded on 03/23/2014.
- Spring '14