Time-Series Forecast Models EXAMPLE Monthly Sales ( in units ) Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Data Point or (observation) MGMT E-5070.

Time-Series Forecast ModelsTime-Series Forecast Models

EXAMPLEM

on

thly

Sa

les

( i

n u

nit

s )

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Data Pointor

(observation)

MGMT E-5070Data Mining

andForecast Management

Wallace Garden Supply

Wallace Garden SupplyWallace Garden SupplyMGMT E-5070

Storage shed sales over the

past year were as follows:

Wallace Garden SupplyWallace Garden Supply

REQUIREMENTS:

Predict sales for next January via :

1. The naïvenaïve model .2. The 3 - month moving averagemoving average model .3. The 3 - month weighted moving averageweighted moving average model with weights of 3, 2, 1 respectively .4. The exponential smoothingexponential smoothing model with ά = .75. The trend projectiontrend projection model .6. Which model is the most accurate?

Applied Management Science for Decision Making, 2e © 2014 Pearson Learning Solutions

Forecast Model Options

Performance SummaryPerformance Summary

Naïve

Performance SummaryPerformance Summary

Naïve

BEST ( lowest )

Forecasting with Excel

Forecasting with Excel QM 4

Time-Series Forecast ModelsTime-Series Forecast Models

EXAMPLEM

on

thly

Sa

les

( i

n u

nit

s )

Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

Data Pointor

(observation)

MGMT E-5070Data Mining

andForecast Management

Wallace Garden Supply