Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

Table 4

Comparisons of indices’ predictions for different forecasting models.

Index errors	BPNN	STNN	ERNN	ST-ERNN
SSE
MAE	45.3701	24.9687	37.262647	12.7390
RMSE	54.4564	40.5437	49.3907	37.0693
MAPE	20.1994	11.8947	18.2110	4.1353
MAPE(100)	5.0644	3.6868	4.3176	2.6809
TWSE
MAE	252.7225	140.5971	151.2830	105.6377
RMSE	316.8197	186.8309	205.4236	136.1674
MAPE	3.2017	1.7303	1.8449	1.3468
MAPE(100)	2.2135	1.1494	1.3349	1.2601
KOSPI
MAE	74.3073	56.3309	47.9296	18.2421
RMSE	77.1528	58.2944	50.8174	21.0479
MAPE	16.6084	12.4461	10.9608	4.2257
MAPE(100)	7.4379	5.9664	4.9176	2.1788
Nikkei225
MAE	203.8034	138.1857	166.2480	68.5458
RMSE	238.5933	169.7061	207.3395	89.0378
MAPE	1.8556	1.2580	1.5398	0.6010
MAPE(100)	0.7674	0.5191	0.4962	0.4261