A comprehensive evaluation of ensemble learning for stock-market prediction

Nti, Isaac Kofi; Adekoya, Adebayo Felix; Weyori, Benjamin Asubam

doi:10.1186/s40537-020-00299-5

Journal of Big Data

Table 13 Bagging ensemble classifiers training time, predicting time and error metrics result on JSE dataset

From: A comprehensive evaluation of ensemble learning for stock-market prediction

Models	No. of estimators	Mean	STD	RMSE	MAE	R²	Precision	Recall	AUC	Training time	Test time
DTBagc:	1	0.698	0.040	0.412	0.170	0.319	0.835	0.830	0.829	0.366	0.012
SVMBagc:	1	0.555	0.033	0.683	0.467	− 0.874	0.532	0.533	0.502	0.335	0.013
MLPBagc:	1	0.809	0.057	0.436	0.190	0.235	0.786	0.810	0.805	2.280	0.187
DTBagc:	5	0.736	0.032	0.185	0.034	0.862	0.964	0.966	0.965	0.490	0.028
SVMBagc:	5	0.585	0.062	0.683	0.467	− 0.874	0.532	0.533	0.502	1.551	0.039
MLPBagc:	5	0.864	0.017	0.436	0.190	0.235	0.790	0.810	0.805	11.782	1.043
DTBagc:	10	0.751	0.034	0.145	0.021	0.916	0.993	0.979	0.980	0.763	0.053
SVMBagc:	10	0.586	0.069	0.669	0.448	− 0.798	0.575	0.552	0.549	2.840	0.066
MLPBagc:	10	0.863	0.022	0.432	0.187	0.250	0.801	0.813	0.810	23.923	1.489
DTBagc:	15	0.772	0.027	0.107	0.011	0.954	0.989	0.989	0.989	0.978	0.107
SVMBagc:	15	0.568	0.047	0.650	0.423	− 0.698	0.564	0.577	0.556	4.869	0.124
MLPBagc:	15	0.867	0.022	0.428	0.183	0.266	0.802	0.817	0.814	39.900	3.282
DTBagc:	20	0.760	0.032	0.123	0.015	0.939	0.989	0.985	0.985	1.502	0.245
SVMBagc:	20	0.563	0.049	0.626	0.392	− 0.576	0.586	0.608	0.589	5.280	0.236
MLPBagc:	20	0.867	0.024	0.434	0.189	0.243	0.802	0.811	0.808	39.325	2.625
DTBagc:	50	0.767	0.038	0.044	0.002	0.992	0.996	0.998	0.998	3.109	0.188
SVMBagc:	50	0.569	0.059	0.683	0.467	− 0.874	0.532	0.533	0.502	18.013	0.362
MLPBagc:	50	0.869	0.021	0.432	0.187	0.250	0.803	0.813	0.810	132.852	10.885
DTBagc:	100	0.770	0.034	0.000	0.000	1.000	1.000	1.000	1.000	8.923	0.432
SVMBagc:	100	0.568	0.059	0.679	0.461	− 0.851	0.536	0.539	0.508	39.565	0.802
MLPBagc:	100	0.868	0.022	0.434	0.189	0.243	0.802	0.811	0.808	270.825	22.026
DTBagc:	150	0.770	0.031	0.000	0.000	1.000	1.000	1.000	1.000	11.446	0.432
SVMBagc:	150	0.565	0.056	0.679	0.461	− 0.851	0.536	0.539	0.508	58.771	0.802
MLPBagc:	150	0.868	0.020	0.434	0.189	0.243	0.802	0.811	0.808	395.453	22.026
DTBagc:	200	0.771	0.034	0.000	0.000	1.000	1.000	1.000	1.000	13.760	0.772
SVMBagc:	200	0.563	0.056	0.683	0.467	− 0.874	0.532	0.533	0.502	63.858	1.388
MLPBagc:	200	0.870	0.023	0.434	0.189	0.243	0.798	0.811	0.808	548.328	33.102

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