FORECASTING THROUGH TIME SERIES

8/7/2019 FORECASTING THROUGH TIME SERIES

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FORECASTING


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Planning is a fundamental activity of manager.

Forecasting forms the basis of Planning.Be it for

planning for sales & marketing

Production planning

Planning for manpower

forecast is extremely important


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Forecasting is a scientifically calculated guess.

SUPPOSESENIOR MANAGERstates:

My salesman X looks out of window & gives me thesales forecast for next year

This doesn't means X is forecasting he is justpredicting the future sales .

Forecasting is something more scientific than the

looking into the crystal ball and predicting the future.Y = f(T)

Y = variable under forecasting

T = time ( chronologically)


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TYPESOFFORECAST

SHORTRANGEFORECASTup to 1 year ; usually less than 3 month

job scheduling, worker assignment

MEDIUM RANGEFORECAST3 months to 3 years

sales & production planning , budgeting

LONGRANGEFORECAST3+ years

new product planning , facility location


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APPROCHES

QUANTITATIVEQUALITATIVE

Used when situation is vague &

little data is exist

Involves intution, experience

JURY OFEXECUTIVEOPNIONDELPHI METHODSALESFORCECOMPOSITECONSUMER MARKETSURVEY

Used when situation is stable &

historical data exist

Involves mathematical techniques

NAVEAPPROACHMOVING AVERAGE timeEXPONENTIAL SMOTHING seriesTREND PROJECTIONLINEARREGRESSION


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COMPONENTS TIME SERIESTREND CYCLICAL

sales

time

SEASONAL IRREGULAR

episodic : are unpredictable but

can be identified (strike..)

residual : also called chance fluctuation

unpredictable & cant be

identifiedSales


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The 4 component could be interact in 2 waysas

additive model

Y = T + S + C + IIn additive ,component are measured in absolute valuemultiplicative model

Y= T x S x C x I

T = 63.90; S= 61.61%; C=98.77%; I = 66.86%Y= 63.90 x 0.6161 x 0.9877 x 0.6686


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TRENDVALUEEstimate the function as:

Y = f (Ti)

taking the form as

linear : Yi = a + bTi

Exponential : log Yi = A + BTi

Quadratic : Yi = a + bTi + cTi2

Cubic : Yi = a + bTi + cTi2 +dTi3

Choose which one yields highest return for

r2 COEFFICIENTOFDETERMINATION

compute Y = a + bT (T = 1,2,3.)

Y gives trend magnitude.


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moving average method

it is a discrete averaging method, where perids in the past beyond a

certain number are considered irrelevant for the analysis.moving average smoothes the flucation of in data.

p M + pM-1 + ------- + p( M-9 )

SMAM = --------------------------------------

10

Weighted moving averagewhen weights are assigned to different period of time in the past.

recency plays a role here so higher weights are assigned to the most recent

figures.

n p M + ( n-1 )pM-1 + ------- +2p(M-n+2) + p( M n+1 )WMAM = ------------------------------------------------------------

n + ( n-1 ) + --------- + 2 + 1


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eg.A firm operates seven amusement park and five gated park. Its combinedattendance (in thouasnd) for at last 12 year is given. A partner asks to study thetrend in attendence. Compute a 3 year moving average and 3 year weighted

moving average with weights of 0.2 , 0.3 , and 0.5 for successive year.year attendance

(000)1993 5761

1994 6148

1995 67831996 7445

1997 7045

1998 11450

1999 11224

2000 11703

2001 118902002 12380

2003 12181

2004 12557


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year attendance 3 year moving average weighted moving average

(000)1993 5761

1994 6148 (5761 + 6148 + 6783) / 3 = 6231 .2(5761)+.3(6148)+.5(6783)

1995 6783 (6148 + 6783 + 7445) / 3 = 6792 .2(6148)+.3(6783)+.5(7445)

1996 7445 (6783 + 7445 + 7045) / 3 = 7211 .2(6783)+.3(7445)+.5(7405)

1997 7045 ------ --------

1998 11450 ------- --------

1999 11224

2000 11703

2001 11890

2002 12380

2003 12181 (11890 + 12380 + 12557) /3 = 12373 .2(12380)+.3(12181)+.5(12557)

2004 12557


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Taking another example

year Quarter sales four quarter centered

moving averege moving average

1 1 4.8

2 4.1

3 6.0 5.350 5.745

4 6.5 5.600 5.738

2 1 5.8 5.875 5.975

2 5.2 6.075 6.188

3 6.8 6.300 6.325

4 7.4 6.350 6.400

3 1 6.0 6.450 6.538

2 5.6 6.625 6.675

3 7.5 6.725 6.736

4 7.8 6.800 6.8384 1 6.3 6.875 6.938

2 5.9 7.000 7.075

3 8.0 7.150

4 8.4


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SEASONAL INDEX

Quarter seasonal Irregularities Seasonal Indexcomponent values ( St )

( St , It )

1 0.971 , 0.918 , 0.908 0.93

2 0.840 , 0.839 , 0.834 0.84

3 1.096 , 1.075 , 1.109 1.09

4 1.330 , 1.156 , 1.141 1.14


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EXPONENTIAL SMOOTHING

It uses a weighted average of past time series values as the forecast ; it

is a special average case of WMA in which we select only one average the weight of most recent observation. Widely used accurate method.

F t+1 = Dt+(1-)Ft

F t+1 = forecast for next period

Dt = actual demand for present period

Ft = previously determined forecast for present period

= weighting constant , smoothing constant


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forecast , F t+1period month demand = 0.3 = 0.5

1 jan 37 - -

2 feb 40 37.00 37.003 mar 41 37.90 38.50

4 apr 37 38.83 39.75

5 may 45 38.28 38.37

6 jun 50 40.29 41.687 jul 43 43.20 45.84

8 aug 47 43.14 44.42

9 sep 56 44.30 45.71

10 oct 52 47.81 50.8511 nov 55 49.06 51.42

12 dec 54 50.84 53.21

13 jan - 51.79 53.61


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NAVEAPPROACH

Assumes demand in next period is the same as

demand in most recent period

Daug = 48Then Daug = Dsep

sometimes cost effective & efficient


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Any question


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FORECASTING THROUGH TIME SERIES

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