Abstract:
Time series models such as Auto-Regressive Integrated Moving Average (ARIMA)
models are the most important forecasting models used in financial market forecasting
over the past three decades. However, the recent studies show that the combination
ARIMA models with different kind of models can be an effective method of
improving upon their predictive performance. Due to factors of uncertainty from the
integral environment and rapid development of new technology, little data in a short
time period to forecast future situations is encourage. This study is mainly concerned
on the effectiveness of ARIMA and combination fuzzy ARIMA (F ARIMA)
forecasting model, which combines the time series ARIMA model and the fuzzy
regression model. Both of these models are applied in forecast the foreign exchange
rates of Malaysian Ringgit to US dollars. F ARIMA model includes both interval
models with interval parameters and the possible distribution of future value. Based
on the results, it can be shown that the combination F ARIMA can makes a good
forecast by using fewer observations than the ARIMA model.
