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# Forecasting presentation

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forecasting about tropical fruit in malaysia

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### Forecasting presentation

1. 1. NAME NO.MATRIX ELIS ERVINA BINTI SULIMAN NORHIDAYAH BINTI ZULKEFLI
2. 2. INTRODUCTION *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 tropical fruits. *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 Criterion (BIC).
3. 3. DESCRIPTION DATA  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 evaluation part)
4. 4. DATA ANALYSIS Graph of initial data from January 1990 until September 1996.
5. 5. Table ACF and PACF:
6. 6. After First Difference:
7. 7. Table ACF and PACF:
8. 8. Five models have been identified and estimated using Eview STATISTICAL MODEL ARIMA(0, 1, 1) ARIMA(2, 1, 1) ARIMA(2, 1, 0) ARIMA(1, 1, 0) ARIMA(1, 1, 1) AIC 0.015470 -0.090462 0.115171 0.127253 -0.120246 SBC 0.075021 0.030394 0.205813 0.187239 -0.030267 MSE 0.058013 0.050881 0.063266 0.064854 0.050019
9. 9. DYNAMIC FORECAST Estimation: MEASURE ERROR MODEL ARIMA(0, 1, 1) ARIMA(2, 1, 1) ARIMA(1, 1, 1) MSE 0.073645 0.067447 0.067103 RMSE 0.271377 0.259705 0.259042 MAPE 98.89542 99.98908 97.78867
10. 10. Evaluation: MEASURE ERROR MODEL ARIMA(0, 1, 1) ARIMA(2, 1, 1) ARIMA(1, 1, 1) MSE 0.210160 0.210476 0.209699 RMSE 0.458432 0.458777 0.457929 MAPE 111.5229 109.6942 106.2585
11. 11. RESULT MEASURE ERROR MODEL UNIVARIATE MODEL (Holt- Winter) ARIMA(1, 1, 1) MSE 0.19 0.209699 RMSE 0.43 0.457929 MAPE(%) 100.93 106.2585
12. 12. CONCLUSION 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.