teknik peramalan materi minggu kedelapan n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Teknik Peramalan: Materi minggu kedelapan PowerPoint Presentation
Download Presentation
Teknik Peramalan: Materi minggu kedelapan

Loading in 2 Seconds...

play fullscreen
1 / 21

Teknik Peramalan: Materi minggu kedelapan - PowerPoint PPT Presentation


  • 202 Views
  • Uploaded on

Teknik Peramalan: Materi minggu kedelapan.  Model ARIMA Box-Jenkins  Identification of STATIONER TIME SERIES  Estimation of ARIMA model  Diagnostic Check of ARIMA model  Forecasting

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Teknik Peramalan: Materi minggu kedelapan' - brinly


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
teknik peramalan materi minggu kedelapan
Teknik Peramalan: Materi minggu kedelapan

 Model ARIMA Box-Jenkins

 Identification of STATIONER TIME SERIES  Estimation of ARIMA model  Diagnostic Check of ARIMA model  Forecasting

 Studi Kasus : Model ARIMAX (Analisis Intervensi, Fungsi Transfer dan Neural Networks)

general theoretical acf and pacf of arima models
General Theoretical ACF and PACF of ARIMA Models

ModelACFPACF

MA(q): moving average of order qCuts offDies downafter lag q

AR(p): autoregressive of order pDies downCuts offafter lag p

ARMA(p,q): mixed autoregressive-Dies downDies downmoving average of order (p,q)

AR(p) or MA(q)Cuts offCuts offafter lag qafter lag p

No order AR or MANo spikeNo spike(White Noise or Random process)

theoretically of acf and pacf of the first order moving average model or ma 1
Theoretically of ACF and PACF of The First-order Moving Average Model or MA(1)

The modelZt =  + at – 1 at-1 , where = 

 Invertibility condition : –1 < 1 < 1

Theoretically of PACF

Theoretically of ACF

theoretically of acf and pacf of the first order moving average model or ma 1 graphics illustration
Theoretically of ACF and PACF of The First-order Moving Average Model or MA(1) … [Graphics illustration]

PACF

ACF

PACF

ACF

slide5
Simulation example of ACF and PACF of The First-order Moving Average Model or MA(1) … [Graphics illustration]
theoretically of acf and pacf of the second order moving average model or ma 2
Theoretically of ACF and PACF of The Second-order Moving Average Model or MA(2)

The modelZt =  + at – 1 at-1– 2 at-2 , where = 

 Invertibility condition : 1 + 2< 1 ; 2  1< 1 ; |2|< 1

Theoretically of PACF

Theoretically of ACF

Dies Down(according to a mixture of damped exponentials and/or damped sine waves)

slide7
Theoretically of ACF and PACF of The Second-order Moving Average Model or MA(2) … [Graphics illustration] … (1)

PACF

ACF

PACF

ACF

slide8
Theoretically of ACF and PACF of The Second-order Moving Average Model or MA(2) … [Graphics illustration] … (2)

PACF

ACF

PACF

ACF

slide9
Simulation example of ACF and PACF of The Second-order Moving Average Model or MA(2) …[Graphics illustration]
theoretically of acf and pacf of the first order autoregressive model or ar 1
Theoretically of ACF and PACF of The First-order Autoregressive Model or AR(1)

The modelZt =  + 1 Zt-1 + at, where =  (1-1)

 Stationarity condition : –1 < 1 < 1

Theoretically of PACF

Theoretically of ACF

theoretically of acf and pacf of the first order autoregressive model or ar 1 graphics illustration
Theoretically of ACF and PACF of The First-order Autoregressive Model or AR(1) … [Graphics illustration]

PACF

ACF

PACF

ACF

slide12
Simulation example of ACF and PACF of The First-order Autoregressive Model or AR(1) … [Graphics illustration]
theoretically of acf and pacf of the second order autoregressive model or ar 2
Theoretically of ACF and PACF of The Second-order Autoregressive Model or AR(2)

The modelZt =  + 1 Zt-1 + 2 Zt-2 + at, where = (112)

 Stationarity condition : 1 + 2< 1 ; 2  1< 1 ; |2|< 1

Theoretically of PACF

Theoretically of ACF

slide14
Theoretically of ACF and PACF of The Second-order Autoregressive Model or AR(2) … [Graphics illustration] … (1)

PACF

ACF

PACF

ACF

slide15
Theoretically of ACF and PACF of The Second-order Autoregressive Model or AR(2) … [Graphics illustration] … (2)

PACF

ACF

PACF

ACF

slide16
Simulation example of ACF and PACF of The Second-order Autoregressive Model or AR(2) …[Graphics illustration]
theoretically of acf and pacf of the mixed autoregressive moving average model or arma 1 1
Theoretically of ACF and PACF of The Mixed Autoregressive-Moving Average Model or ARMA(1,1)

The modelZt =  + 1 Zt-1 + at  1 at-1, where =  (11)

 Stationarity and Invertibility condition : |1|< 1 and |1|< 1

Theoretically of PACF

Theoretically of ACF

Dies Down(in fashion dominated by damped exponentials decay)

slide18
Theoretically of ACF and PACF of The Mixed Autoregressive-Moving Average Model or ARMA(1,1) …[Graphics illustration] … (1)

ACF

PACF

ACF

PACF

slide19
Theoretically of ACF and PACF of The Mixed Autoregressive-Moving Average Model or ARMA(1,1) … [Graphics illustration] … (2)

PACF

ACF

PACF

ACF

slide20
Theoretically of ACF and PACF of The Mixed Autoregressive-Moving Average Model or ARMA(1,1) …[Graphics illustration] … (3)

PACF

ACF

ACF

PACF

slide21
Simulation example of ACF and PACF of The Mixed Autoregressive-Moving Average Model or ARMA(1,1) …[Graphics illustration]