PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)
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
Downloads
Downloads
Published
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in JUTI unless they receive approval for doing so from the Editor-in-Chief.
JUTI open access articles are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
