Teknik Peramalan: Materi minggu kedua

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Teknik Peramalan Materi minggu kedua

• Pendahuluan
• Naïve Models dan Moving Average Methods
• Exponential Smoothing Methods
• Regresi dan Trend Analysis
• Regresi Berganda dan Time Series Regresi
• Metode Dekomposisi
• Model ARIMA Box-Jenkins
• Studi Kasus Model ARIMAX (Analisis Intervensi,
  Fungsi Transfer dan Neural Networks)

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Referensi Utama

• ?. Hanke, J.E. and Reitsch, A.G. (1995 2001)
  Business
  Forecasting
  5th and 7th edition,
  Prentice Hall.
• ? Chapter 4 Exploring Data Pattern
• ? Measuring Forecasting Error
• ? Chapter 5 Moving Average and Smoothing
  Methods
• ? Naïve Models
• ? Averaging Methods
• ? Exponential Smoothing Methods

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Kaitan Pola Data dengan Metode Peramalan
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Naïve Model

• ? The recent periods are the best predictors of
  the future.
• 1. The simplest model for stationary data is
• 2. The simplest model for trend data is
• or
• 3. The simplest model for seasonal data is

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MINITAB implementation
Time Series Plot
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MINITAB implementation
(continued)
Naïve 3
Naïve 1
Naïve 2
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MINITAB implementation
(continued)
Naïve 1
Naïve 2
Naïve 3
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MINITAB implementation
(continued)
Naïve 1
Naïve 2
Naïve 3
MSE.1 28547.5, MSE.2 53592.5, MSE.3
4567.5
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Measuring Forecasting Error

• ? MSE/MSD (mean squared error)
• ? rata-rata kuadrat kesalahan (residual atau
  error).
• ? MAD (mean absolute deviation)
• ? ukuran kesalahan peramalan dalam unit ukuran
  yang sama dengan data aslinya.
• ? MAPE (mean absolute percentage error)
• ? persentase kesalahan absolut rata-rata.
• ? MPE (mean percentage error)
• ? persentase kesalahan rata-rata.

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Average Methods

• 1. Simple Averages
• ? obtained by finding the mean for all the
  relevant values and then using this mean to
  forecast the next period.
• 2. Moving Averages
• ? obtained by finding the mean for a specified
  set of values and then using this mean to
  forecast the next period.

for stationary data
for stationary data
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Average Methods
(continued)

• 3. Double Moving Averages
• ? one set of moving averages is computed, and
  then a second set is computed as a moving
  average of the first set.
• (i).
• (ii).
• (iii).
• (iv).

for a linear trend data
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MINITAB implementation
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MINITAB implementation
(continued)
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Case Study of Video Store MINITAB implementation
Double Moving Averages
Moving Averages
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Moving Averages Result
(continued)
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Moving Averages VS Double Moving Averages
Results
MA or Moving Averages
DMA or Double Moving Averages
MSE.MA 132.67, MSE.DMA 63.7
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Exponential Smoothing Methods

• Single Exponential Smoothing ? for stationary
  data
• Exponential Smoothing Adjusted for Trend Holts
  Method
• 1. The exponentially smoothed series
• At ? Yt (1??) (At-1 Tt-1)
• 2. The trend estimate
• Tt ? (At ? At-1) (1 ? ?) Tt-1
• 3. Forecast p periods into the future

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Exponential Smoothing Adjusted for Trend and
Seasonal Variation Winters Method

• 1. The exponentially smoothed series
• 2. The trend estimate
• 3. The seasonality estimate
• 4. Forecast p periods into the future

Three parameters models
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SES MINITAB implementation
SES dengan alpha 0,1
SES dengan alpha 0,6
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SES MINITAB implementation
(continued)
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SES MINITAB implementation
(continued)
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DES (Holts Methods) MINITAB implementation
(continued)
DES dengan alpha 0,3 dan beta 0,1
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DES MINITAB implementation
(continued)
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Winters Methods MINITAB implementation
Winters Methods dengan alpha 0,4 beta 0,1 dan
gamma 0,3
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Winters Methods MINITAB implementation
(continued)