Teknik Peramalan: Materi minggu kesepuluh

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Teknik Peramalan Materi minggu kesepuluh

• ? Model ARIMA Box-Jenkins untuk data time series
  yang TIDAK STASIONER
• ? Identification of NONSTATIONARY TIME SERIES
  
  ? Estimation of ARIMA model
  ? Diagnostic Check
  of ARIMA model
  ? Forecasting
• ? Studi Kasus Model ARIMAX (Analisis
  Intervensi, Fungsi Transfer dan Neural Networks)

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Example Weekly sales of Ultra Shine toothpaste
(in units of 1000 tubes) Bowerman and
OConnell, pg. 478
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Example IDENTIFICATION step
stationarity and ACF
Dying down extremely slowly
ACF
Nonstationary time series
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Example IDENTIFICATION step
Difference Wt Yt Yt-1
Wt ? AR(1) or Yt ? ARI(1,1)
Stationary time series
ACF
PACF
Dies down
Cuts off after lag 1
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Example ESTIMATION and DIAGNOSTIC CHECK step

Yt 3.0232 1.6591 Yt-1 0.6591 Yt-2 at
Estimation and Testing parameter
Diagnostic Check (white noise residual)
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Example DIAGNOSTIC CHECK step Normality test
of residuals
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Example FORECASTING step
MINITAB output
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Comparison ARIMA versus Trend Analysis
ARIMA(1,1,0) MSE 7.647
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Plot comparison ARIMA versus Trend Analysis
ARIMA(1,1,0) MSE 7.647
Trend Analysis MSE 598.212
Forecast comparison
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Plot RESIDUAL comparison ARIMA versus Trend
Analysis
ARIMA(1,1,0) MSE 7.647
Trend Analysis MSE 598.212
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MINITAB command IDENTIFICATION Step
Plot Data ? stationarity data
To make stationarity data
ACF PACF data ? to find tentative
ARIMA model