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# Hopefully objective forecasting methods two primary

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Unformatted text preview: (hopefully) Objective Forecasting Methods Two primary methods: causal models and time series causal and methods methods Causal Models: Let Y be the quantity to be forecasted and (X1, X2, . . . , Xn) be n variables that have predictive power be for Y. A causal model is Y = f (X1, X2, . . . , Xn). (X ). A typical relationship is a linear one. That is, typical Y = a0 + a1X1 + . . . + an Xn. 4 Time Series Methods A time series is just collection of past values of the time variable being predicted. Also known as naive methods. Goal is to isolate patterns in past data. Trend Trend Seasonality Seasonality Cycles Cycles Randomness Randomness Time Series Patterns 5 Notation Conventions Let D1, D2, . . . Dn, . . . be the past values of the series to Let be be predicted (observed demand). If we are making a forecast in period t, assume we have observed Dt , Dt-1 etc. etc. Let Ftt, t + τ be forecast made in period t for the demand in Let , forecast period t + τ where τ = 1, 2, 3, … 1, Then Ft -1, t is the forecast made in t-1 for t and Ft, t+1 Then 1, is iis the forecast made in t for t+1. (one step ahead) Use s shorthand notation Ft = Ft - 1, t . shorthand Evaluation of Forecasts Evaluation The forecast error in period t, et, iis the difference s 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. For MAD = (1/n) Σ | ei | MSE = (1/n) Σ ei 2 6 Example Compare the accuracy of the forecasts of two managers Compare of a SRAM manufac...
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## This document was uploaded on 03/23/2014.

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