ELIS ERVINA BINTI SULIMAN
NORHIDAYAH BINTI ZULKEFLI
*The data will helps us to forecast the price of tropical fruits
for the next period.
*Box Jenkins ARIMA modeling approach is followed (Harvey,
1993) to generate the forecast of the monthly price of
*The final models that used for forecasting are determined
by a number of diagnostic statistics including the Mean
Squared Error (MSE), Root Mean Squared Error (RMSE),
Akaike Information Criterion (AIC) and Bayesian Information
Focused on the topic tropical fruits in Malaysia
from January 1990 to December 1998.
It divided into fitted and hold out parts ( January
1990 until September 1996 is for estimation part
while October 1996 up to December 1998 is for
Graph of initial data from January 1990 until September
MEASURE ERROR MODEL
ARIMA(1, 1, 1)
MSE 0.19 0.209699
RMSE 0.43 0.457929
MAPE(%) 100.93 106.2585
Based on error measure, the univariate
model which is Holt-Winter is shown
the smallest error measure. For MSE is
0.19, RMSE IS 0.43 and MAPE is 100.93.
We can say that the univariate model is
the best model for forecasting the
future price of tropical fruits.