Table 3 Forecast accuracy for the 7... | Emerald Publishing

Table 3

Forecast accuracy for the 7 series' level and log-transformed training samples under one-step forecast

Forecast method 1 – ARIMA for training sample of level series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite ARIMA(3,1,2)	Singapore ARIMA(1,0,3)	Vietnam ARIMA(0,1,4)	Thailand ARIMA(1,1,0)	The Philippines ARIMA(0,1,2)	Malaysia ARIMA(1,1,0)	Indonesia ARIMA(3,1,2)	Average
RMSE	30.23	4.006	10.255	6.852	18.669	8.629	9.902	12.649
MAPE	3.095	2.281	4.517	3.491	33.784	2.442	2.855	7.495
ACF1	0.021	−0.061	−0.059	2.22E−05	−0.005	−0.001	0.007	−0.014

Forecast method 1 – ARIMA for training sample of level series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite ARIMA(3,1,2)	Singapore ARIMA(1,0,3)	Vietnam ARIMA(0,1,4)	Thailand ARIMA(1,1,0)	The Philippines ARIMA(0,1,2)	Malaysia ARIMA(1,1,0)	Indonesia ARIMA(3,1,2)	Average
RMSE	30.23	4.006	10.255	6.852	18.669	8.629	9.902	12.649
MAPE	3.095	2.281	4.517	3.491	33.784	2.442	2.855	7.495
ACF1	0.021	−0.061	−0.059	2.22E−05	−0.005	−0.001	0.007	−0.014

Forecast method 2 – NNAR for training sample of level series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite SNNAR(2,1,2)	Singapore SNNAR(2,1,2)	Vietnam SNNAR(2,1,2)	Thailand SNNAR(2,1,2)	The Philippines SNNAR(7,1,4)	Malaysia SNNAR(2,1,2)	Indonesia SNNAR(2,1,2)	Average
RMSE	21.787	2.818	8.387	5.423	6.366	6.886	8.768	8.634
MAPE	2.393	1.546	4.049	2.88	29.454	2.122	2.469	6.416
ACF1	−0.009	−0.073	0.006	−0.125	−0.222	−0.14	0.051	−0.073

Forecast method 2 – NNAR for training sample of level series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite SNNAR(2,1,2)	Singapore SNNAR(2,1,2)	Vietnam SNNAR(2,1,2)	Thailand SNNAR(2,1,2)	The Philippines SNNAR(7,1,4)	Malaysia SNNAR(2,1,2)	Indonesia SNNAR(2,1,2)	Average
RMSE	21.787	2.818	8.387	5.423	6.366	6.886	8.768	8.634
MAPE	2.393	1.546	4.049	2.88	29.454	2.122	2.469	6.416
ACF1	−0.009	−0.073	0.006	−0.125	−0.222	−0.14	0.051	−0.073

Forecast method 1 – ARIMA for training sample of log-transformed series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite ARIMA(0,1,3)	Singapore ARIMA(5,0,0)	Vietnam ARIMA(1,1,0)	Thailand ARIMA(1,1,0)	The Philippines ARIMA(0,1,2)	Malaysia ARIMA(1,1,0)	Indonesia ARIMA(3,1,2)	Average
RMSE	0.045	0.032	0.065	0.05	1.867	0.036	0.037	0.305
MAPE	0.494	0.475	0.904	0.723	16.935	0.456	0.508	2.928
ACF1	−0.009	−0.094	0.006	−0.016	−0.005	−0.019	−0.006	−0.020

Forecast method 1 – ARIMA for training sample of log-transformed series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite ARIMA(0,1,3)	Singapore ARIMA(5,0,0)	Vietnam ARIMA(1,1,0)	Thailand ARIMA(1,1,0)	The Philippines ARIMA(0,1,2)	Malaysia ARIMA(1,1,0)	Indonesia ARIMA(3,1,2)	Average
RMSE	0.045	0.032	0.065	0.05	1.867	0.036	0.037	0.305
MAPE	0.494	0.475	0.904	0.723	16.935	0.456	0.508	2.928
ACF1	−0.009	−0.094	0.006	−0.016	−0.005	−0.019	−0.006	−0.020

Forecast method 2 – NNAR for training sample of log-transformed series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite SNNAR(2,1,2)	Singapore SNNAR (1,1,2)	Vietnam SNNAR (2,1,2)	Thailand SNNAR (2,1,2)	The Philippines SNNAR(7,1,4)	Malaysia SNNAR(2,1,2)	Indonesia SNNAR(2,1,2)	Average
RMSE	0.031	0.022	0.058	0.039	0.657	0.029	0.034	0.124
MAPE	0.37	0.332	0.798	0.587	9.176	0.384	0.446	1.728
ACF1	0.017	0.15	0.012	−0.084	−0.097	−0.083	0.047	−0.005

Forecast method 2 – NNAR for training sample of log-transformed series at one-step forecast
Accuracy measure	Routes
Accuracy measure	SEAFI composite SNNAR(2,1,2)	Singapore SNNAR (1,1,2)	Vietnam SNNAR (2,1,2)	Thailand SNNAR (2,1,2)	The Philippines SNNAR(7,1,4)	Malaysia SNNAR(2,1,2)	Indonesia SNNAR(2,1,2)	Average
RMSE	0.031	0.022	0.058	0.039	0.657	0.029	0.034	0.124
MAPE	0.37	0.332	0.798	0.587	9.176	0.384	0.446	1.728
ACF1	0.017	0.15	0.012	−0.084	−0.097	−0.083	0.047	−0.005

Source(s): Authors work