Business Forecasting_Naive Method

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BUSINESSFORECASTINGNAVE METHOD

Puput Tri Komalasari

Quantitative Methodologies

Quantitative methods model historical demandvariation patterns (random, trend, seasonal or cyclical).Once past history has been explained by a model,extrapolations can be made about the future.

Some simplistic techniques are:

Averaging/Smoothing Models

Nave Moving average

Weighted moving average

Exponential smoothing

These techniques are best used when demand is stablewith no trend or seasonal pattern


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FORECASTING TECHNIQUE

In some series, naive model works as well as complex models.

Naive model also used as a benchmark model, more sophisticated

models must perform better than the naive model

Nave is the basis for comparison of all methods.

NAVE METHOD

Assumes demand in next period is the same asdemand in most recent period e.g., If January sales were 68, then February

sales will be 68

Tomorrow will be like todaySimplest possible forecast Sometimes cost effective and efficientCan be good starting

point

Ignores any historical data previous to today

Horizon: Short range

Strength: Low cost, quick and easy to use, simple and easy tounderstand

Weakness: Not very accurate when a longer forecastinghorizon is necessary


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NAVE MODEL I

The simplest model is to assume that the next periodwill be identical to the present period.

= 1 or +1 =

where F = Forecasted value

A = Actual value

Tomorrows weather will be similar to today

weather

UNEMPLOYMENT RATEYear Quarter UR Year Quarter UR

1990 1 5.3 1995 1 5.5

1990 2 5.3 1995 2 5.7

1990 3 5.7 1995 3 5.7

1990 4 6.1 1995 4 5.6

1991 1 6.6 1996 1 5.5

1991 2 6.8 1996 2 5.5

1991 3 6.9 1996 3 5.3

1991 4 7.1 1996 4 5.3

1992 1 7.4 1997 1 5.3

1992 2 7.6 1997 2 5.0

1992 3 7.6 1997 3 4.9

1992 4 7.4 1997 4 4.7

1993 1 7.1 1998 1 4.7

1993 2 7.1 1998 2 4.4

1993 3 6.8 1998 3 4.5

1993 4 6.6 1998 4 4.4

1994 1 6.6 1999 1 4.3

1994 2 6.2 1999 2 4.3

1994 3 6.0 1999 3 4.2

1994 4 5.6 1999 4 4.1

2000 1 4.0


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UNEMPLOYMENT RATE

UNEMPLOYMENT RATE

Ye ar Quarte r UR Forecasted

UR

1990 1 5.3

1990 2 5.3 5.3

1990 3 5.7 5.3

1990 4 6.1 5.7

1991 1 6.6 6.1

1991 2 6.8 6.6

1991 3 6.9 6.8

1991 4 7.1 6.9

1992 1 7.4 7.1

1992 2 7.6 7.41992 3 7.6 7.6

1992 4 7.4 7.6

1993 1 7.1 7.4

1993 2 7.1 7.1

1993 3 6.8 7.1

1993 4 6.6 6.8

1994 1 6.6 6.6

1994 2 6.2 6.6

1994 3 6.0 6.2

1994 4 5.6 6.0

Ye ar Quarte r UR Forecasted

UR

1995 1 5.5 5.6

1995 2 5.7 5.5

1995 3 5.7 5.7

1995 4 5.6 5.7

1996 1 5.5 5.6

1996 2 5.5 5.5

1996 3 5.3 5.5

1996 4 5.3 5.3

1997 1 5.3 5.3

1997 2 5.0 5.3

1997 3 4.9 5.0

1997 4 4.7 4.9

1998 1 4.7 4.7

1998 2 4.4 4.7

1998 3 4.5 4.4

1998 4 4.4 4.5

1999 1 4.3 4.4

1999 2 4.3 4.3

1999 3 4.2 4.3

1999 4 4.1 4.2

2000 1 4.0 4.1


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HOW GOOD THE FORECASTS?

Actual and forecastedvalues seems very close.

But, errors exhibitssystematic pattern.

If the series has trendbehavior, the forecastsare always underestimateor overestimate thevalues.

Direction of the seriesshould be added intoforecasting model

NAIVE MODEL II

The naive model i can not handle trends.

The naive I model revized in order to mimic the trendbehaviour of the series.

The second simple model is to assume that the nextperiods value will be current value and some

proportion of the last change

Where F = Forecasted value

A = actual value

P = adjustment coefficient 0 < p < 1


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UNEMPLOYMENT RATENAVE MODEL II

FORECASTS

Y ear Quarter UR Forecasted

UR Error

1990 1 5.3

1990 2 5.3

1990 3 5.7 5.30 0.40

1990 4 6.1 5.94 0.16

1991 1 6.6 6.34 0.26

1991 2 6.8 6.90 -0.10

1991 3 6.9 6.92 -0.02

1991 4 7.1 6.96 0.14

1992 1 7.4 7.22 0.18

1992 2 7.6 7.58 0.02

1992 3 7.6 7.72 -0.12

1992 4 7.4 7.60 -0.20

1993 1 7.1 7.28 -0.18

1993 2 7.1 6.92 0.18

1993 3 6.8 7.10 -0.30

1993 4 6.6 6.62 -0.02

1994 1 6.6 6.48 0.12

1994 2 6.2 6.60 -0.40

1994 3 6.0 5.96 0.04

1994 4 5.6 5.88 -0.28

1995 1 5.5 5.36 0.14

1995 2 5.7 5.44 0.26

1995 3 5.7 5.82 -0.12

1995 4 5.6 5.70 -0.10

1996 1 5.5 5.54 -0.04

1996 2 5.5 5.44 0.06

1996 3 5.3 5.50 -0.20

1996 4 5.3 5.18 0.12

1997 1 5.3 5.30 0.00

1997 2 5.0 5.30 -0.30

1997 3 4.9 4.82 0.08

1997 4 4.7 4.84 -0.14

1998 1 4.7 4.58 0.12

1998 2 4.4 4.70 -0.30

1998 3 4.5 4.22 0.28

1998 4 4.4 4.56 -0.16

1999 1 4.3 4.34 -0.04

1999 2 4.3 4.24 0.06

1999 3 4.2 4.30 -0.10

1999 4 4.1 4.14 -0.04

2000 1 4.0 4.04 -0.04

HOW GOOD THE FORECASTS?

Actual and forecastedvalues seems very close.

But, errors still exhibitssystematic pattern.

There is an interchangeof the signs of the error

terms Can we solve this

problem and get betterforecasts by changingthe adjustmentcoefficient?


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Year Quarter URForecasted

UR, P=0.3

Forecasted

UR, P=0.6

Forecasted

UR, P=0.9

Error,

P=0.3

Error,

P=0.6

Error,

P=0.9

1990 1 5.3

1990 2 5.3

1990 3 5.7 5.30 5.30 5.30 0.40 0.40 0.40

1990 4 6.1 5.82 5.94 6.06 0.28 0.16 0.04

1991 1 6.6 6.22 6.34 6.46 0.38 0.26 0.14

1991 2 6.8 6.75 6.90 7.05 0.05 (0.10) (0.25)

1991 3 6.9 6.86 6.92 6.98 0.04 (0.02) (0.08)

1991 4 7.1 6.93 6.96 6.99 0.17 0.14 0.11

1992 1 7.4 7.16 7.22 7.28 0.24 0.18 0.121992 2 7.6 7.49 7.58 7.67 0.11 0.02 (0.07)

1992 3 7.6 7.66 7.72 7.78 (0.06) (0.12) (0.18)

1992 4 7.4 7.60 7.60 7.60 (0.20) (0.20) (0.20)

1993 1 7.1 7.34 7.28 7.22 (0.24) (0.18) (0.12)

1993 2 7.1 7.01 6.92 6.83 0.09 0.18 0.27

1993 3 6.8 7.10 7.10 7.10 (0.30) (0.30) (0.30)

1993 4 6.6 6.71 6.62 6.53 (0.11) (0.02) 0.07

1994 1 6.6 6.54 6.48 6.42 0.06 0.12 0.18

1994 2 6.2 6.60 6.60 6.60 (0.40) (0.40) (0.40)

1994 3 6.0 6.08 5.96 5.84 (0.08) 0.04 0.16

1994 4 5.6 5.94 5.88 5.82 (0.34) (0.28) (0.22)

1995 1 5.5 5.48 5.36 5.24 0.02 0.14 0.26

1995 2 5.7 5.47 5.44 5.41 0.23 0.26 0.29

1995 3 5.7 5.76 5.82 5.88 (0.06) (0.12) (0.18)

1995 4 5.6 5.70 5.70 5.70 (0.10) (0.10) (0.10)

1996 1 5.5 5.57 5.54 5.51 (0.07) (0.04) (0.01)

1996 2 5.5 5.47 5.44 5.41 0.03 0.06 0.09

1996 3 5.3 5.50 5.50 5.50 (0.20) (0.20) (0.20)

1996 4 5.3 5.24 5.18 5.12 0.06 0.12 0.18

1997 1 5.3 5.30 5.30 5.30 - - -

1997 2 5.0 5.30 5.30 5.30 (0.30) (0.30) (0.30)

1997 3 4.9 4.91 4.82 4.73 (0.01) 0.08 0.171997 4 4.7 4.87 4.84 4.81 (0.17) (0.14) (0.11)

1998 1 4.7 4.64 4.58 4.52 0.06 0.12 0.18

1998 2 4.4 4.70 4.70 4.70 (0.30) (0.30) (0.30)

1998 3 4.5 4.31 4.22 4.13 0.19 0.28 0.37

1998 4 4.4 4.53 4.56 4.59 (0.13) (0.16) (0.19)

1999 1 4.3 4.37 4.34 4.31 (0.07) (0.04) (0.01)

1999 2 4.3 4.27 4.24 4.21 0.03 0.06 0.09

1999 3 4.2 4.30 4.30 4.30 (0.10) (0.10) (0.10)

1999 4 4.1 4.17 4.14 4.11 (0.07) (0.04) (0.01)

2000 1 4.0 4.07 4.04 4.01 (0.07) (0.04) (0.01)

ACTUAL &ALTERNATIVE FORECASTS


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FORECAST EVALUATION

It is simple to evaluate the forecasts for any givenperiod

But, we need some criterias to evaluate overallperiod or for given a specific period.


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Business Forecasting_Naive Method