Metode Peramalan Deret Waktu STK 352

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Metode Peramalan Deret Waktu – STK 352

Pendugaan parameter dilakukan setelah menentukan model tentatif, berdasarkan data pengamatan π‘Œ1, π‘Œ2, … , π‘Œπ‘›.

Metode yang bisa digunakan:

Metode momen

Metode kuadrat terkecil

Metode kemungkinan maksimum

Method of Moments (MM)

Methods of Moment estimation is a general method where equations for

estimating parameters are found by equating population moments with the

corresponding sample moments:

etc.

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Trivial MM estimates are estimates

of the population mean ( ) and the

population variance ( 2).

The benefit of the method is that the

equations render possibilities to

estimate other parameters.

Model: π‘Œπ‘‘ = πœ™π‘Œπ‘‘βˆ’1 + 𝑒𝑑

πœ™ = π‘Ÿ1

πœŒπ‘˜ = πœ™π‘˜ untuk π‘˜ = 1,2, … 𝜌1 = πœ™ 𝜌1 = πœ™

π‘Œπ‘‘ = πœ™1π‘Œπ‘‘βˆ’1 + πœ™2π‘Œπ‘‘βˆ’2 + β‹― + πœ™π‘π‘Œπ‘‘βˆ’π‘ + 𝑒𝑑

πΆπ‘œπ‘£(π‘Œπ‘‘, π‘Œπ‘‘βˆ’π‘˜) = πœ™1πΆπ‘œπ‘£ π‘Œπ‘‘βˆ’1, π‘Œπ‘‘βˆ’π‘˜ + πœ™2πΆπ‘œπ‘£ π‘Œπ‘‘βˆ’2, π‘Œπ‘‘βˆ’π‘˜ + β‹―

+πœ™π‘πΆπ‘œπ‘£(π‘Œπ‘‘βˆ’π‘, π‘Œπ‘‘βˆ’π‘˜) + πΆπ‘œπ‘£(𝑒𝑑 , π‘Œπ‘‘βˆ’π‘˜)

π›Ύπ‘˜ = πœ™1π›Ύπ‘˜βˆ’1 + πœ™2π›Ύπ‘˜βˆ’2 + β‹― + πœ™π‘π›Ύπ‘˜βˆ’π‘

πœŒπ‘˜ = πœ™1πœŒπ‘˜βˆ’1 + πœ™2πœŒπ‘˜βˆ’2 + β‹― + πœ™π‘πœŒπ‘˜βˆ’π‘

dibagi 𝛾0

πœŒπ‘˜ = πœ™1πœŒπ‘˜βˆ’1 + πœ™2πœŒπ‘˜βˆ’2 + β‹― + πœ™π‘πœŒπ‘˜βˆ’π‘ untuk π‘˜ β‰₯ 1

Jika π‘˜ = 1,2, … dengan 𝜌0 = 1 dan πœŒπ‘˜ = πœŒβˆ’π‘˜ , diperoleh

persamaan umum Yule-Walker:

Persamaan yule-walker:

πœŒπ‘˜ = πœ™1πœŒπ‘˜βˆ’1 + πœ™2πœŒπ‘˜βˆ’2 + β‹― + πœ™π‘πœŒπ‘˜βˆ’π‘

Sehingga:

𝜌1 = πœ™1+𝜌1πœ™2 π‘Ÿ1 = πœ™1+π‘Ÿ1 πœ™2

𝜌2 = πœ™1𝜌1+πœ™2 π‘Ÿ2 = πœ™1π‘Ÿ1+ πœ™2

dan

Model: π‘Œπ‘‘ = 𝑒𝑑 βˆ’ πœƒπ‘’π‘‘βˆ’1

𝜌1 = βˆ’πœƒ

1 + πœƒ2 π‘Ÿ1 = βˆ’ πœƒ

1 + πœƒ2

Jika π‘Ÿ1 < 0.5 maka:

πœƒ = βˆ’1

2π‘Ÿ1Β±

1

4π‘Ÿ12 βˆ’ 1 =

βˆ’1 Β± 1 βˆ’ 4π‘Ÿ12

2π‘Ÿ1

Menduga 𝛾0 = π‘‰π‘Žπ‘Ÿ π‘Œπ‘‘ menggunakan ragam contoh:

𝑠2 =1

𝑛 βˆ’ 1

𝑑=1

𝑛

π‘Œπ‘‘ βˆ’ π‘Œ 2

Untuk model AR(p):

πœŽπ‘’2 = 1 βˆ’ πœ™1π‘Ÿ1 βˆ’ πœ™2π‘Ÿ2 βˆ’ β‹― βˆ’ πœ™π‘π‘Ÿπ‘ 𝑠2

Untuk kasus khusus AR(1)

πœŽπ‘’2 = 1 βˆ’ πœ™π‘Ÿ1 𝑠2 = 1 βˆ’ π‘Ÿ1

2 𝑠2

dengan πœ™ = π‘Ÿ1

Untuk kasus MA(q):

πœŽπ‘’2 =

𝑠2

1 + πœƒ12 + πœƒ2

2 + β‹― + πœƒπ‘ž2

Untuk kasus ARMA(1,1):

Misalkan terdapat data deret waktu sbb:

Jika untuk data tersebut menggunakan model AR(1): π‘Œπ‘‘ = 𝛼 + πœ™π‘Œπ‘‘βˆ’1 + 𝑒𝑑

Tentukan penduga parameternya, yaitu 𝛼 dan πœ™ denganmetode momen!

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If the process mean is different than zero

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we expect the plot to suggest a rectangular scatter around a zero horizontal level with no trends whatsoever

Increased variation

Very large magnitudes

quantile-quantile plots are an effective tool for assessing normality

Outliers

To check on the independence of the noise terms in the model, we consider the sample autocorrelation function of the residuals, denoted π‘Ÿπ‘˜.

H0: sisaan saling bebas

H1: sisaan tidak saling bebas

Lakukan prosedur uji

Ljung-Box berdasarkan

informasi di atas !

AIC

BIC

MAPE

MSE

1. Cryer JD, Chan KS. 2008. Time Series Analysis with Application with R. New York: Springer.

2. Pustaka lain yang relevan.

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