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Leivina Kartika Dewi
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Computer Science & IT Dentistry Education Library & Information Science

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PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA) Anggraeni, Wiwik; Dewi, Leivina Kartika
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 2, Juli 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (233.953 KB) | DOI: 10.12962/j24068535.v7i2.a180

Abstract

Forecasting technique is an important component of decision making because it aims to predict values of data in the future. Many existing forecasting methods only predict single variable data and thus do not consider correlation between variable in a dataset. This paper proposes Vector Autoregressive Moving Average (VARMA) as a forecasting method to predict data with more than one variable. This method combines regression concept i.e. autoregressive (VAR) and moving average method (VMA) for multi-variables data. The first step in VARMA method is testing the stationary of the data. Differencing process is conducted in order to change non stationary data to stationary. The next step is to identify the order of the VARMA model of the stationary data. The parameters are then estimated according to the order and co-integration test is conducted on the variables. The model is tested to assess its validity. If the model is valid then forecasting can be done using the model generic formula. The errors of the forecast are calculated to evaluate the performance of the model. Random values are found in the forecast of VARMA method. However, the error remains within a certain interval. The error interval is below 10% so it can be argued that this model is very accurate in predicting the data.   Keywords: Forecasting, Multi-Variables, VAR, VMA, VARM, Co-integration Test
Co-Authors Wiwik Anggraeni