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 14 Bagging and boosting ensemble regressors error metrics result over JSE dataset

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

Models	No. of estimators	RMSE	MAE	R²	EVS	MedAE	RMSLE	Train time	Test time
DTBagr	1	0.2043	0.1426	0.9153	0.9154	0.0932	0.0372	0.005	0.0010
SVMBagr	1	0.0685	0.0641	0.9905	0.9983	0.0691	0.0121	0.008	0.0020
MLPBagr	1	0.0363	0.0277	0.9973	0.9973	0.0218	0.0061	0.338	0.0010
DTBotr	1	0.2344	0.1974	0.8885	0.8886	0.1882	0.0423	0.004	0.0010
SVMBotr	1	0.0681	0.0636	0.9906	0.9982	0.0676	0.0121	0.018	0.0010
MLPBotr	1	0.0339	0.0257	0.9977	0.9977	0.0192	0.0057	1.449	0.0010
DTBagr	5	0.0984	0.0809	0.9803	0.9803	0.0721	0.0175	0.015	0.0020
SVMBagr	5	0.0574	0.0526	0.9933	0.9982	0.0562	0.0101	0.018	0.0010
MLPBagr	5	0.0313	0.0240	0.9980	0.9980	0.0196	0.0053	3.025	0.0030
DTBotr	5	0.1554	0.1175	0.9510	0.9511	0.0827	0.0305	0.014	0.0020
SVMBotr	5	0.0610	0.0559	0.9925	0.9981	0.0590	0.0108	0.043	0.0010
MLPBotr	5	0.0324	0.0249	0.9979	0.9979	0.0195	0.0054	1.467	0.0030
DTBagr	15	0.1076	0.0904	0.9765	0.9765	0.0800	0.0194	0.031	0.0020
SVMBagr	15	0.0460	0.0406	0.9957	0.9981	0.0417	0.0077	0.054	0.0050
MLPBagr	15	0.0292	0.0221	0.9983	0.9983	0.0191	0.0049	7.054	0.0070
DTBotr	15	0.0820	0.0667	0.9864	0.9864	0.0591	0.0141	0.042	0.0040
SVMBotr	15	0.0389	0.0325	0.9969	0.9979	0.0300	0.0064	0.170	0.0080
MLPBotr	15	0.0321	0.0247	0.9979	0.9980	0.0199	0.0054	7.174	0.0060
DTBagr	20	0.1030	0.0877	0.9785	0.9785	0.0802	0.0184	0.030	0.0030
SVMBagr	20	0.0482	0.0430	0.9953	0.9982	0.0444	0.0081	0.061	0.0060
MLPBagr	20	0.0294	0.0223	0.9983	0.9983	0.0189	0.0049	9.403	0.0190
DTBotr	20	0.0746	0.0592	0.9887	0.9887	0.0500	0.0125	0.071	0.0060
SVMBotr	20	0.0443	0.0384	0.9960	0.9981	0.0382	0.0073	0.202	0.0110
MLPBotr	20	0.0319	0.0247	0.9979	0.9980	0.0203	0.0054	10.910	0.0270
DTBagr	50	0.0989	0.0811	0.9801	0.9801	0.0727	0.0174	0.117	0.0100
SVMBagr	50	0.0449	0.0391	0.9959	0.9980	0.0387	0.0074	0.235	0.0190
MLPBagr	50	0.0305	0.0232	0.9981	0.9981	0.0189	0.0051	27.088	0.0210
DTBotr	50	0.0568	0.0444	0.9934	0.9935	0.0368	0.0095	0.133	0.0150
SVMBotr	50	0.0443	0.0384	0.9960	0.9981	0.0382	0.0073	0.357	0.0140
MLPBotr	50	0.0322	0.0258	0.9979	0.9981	0.0229	0.0054	33.004	0.0290
DTBagr	100	0.0988	0.0840	0.9802	0.9802	0.0836	0.0173	0.537	0.0860
SVMBagr	100	0.0442	0.0382	0.9960	0.9980	0.0369	0.0073	1.220	0.0340
MLPBagr	100	0.0303	0.0230	0.9981	0.9981	0.0188	0.0051	52.394	0.0530
DTBotr	100	0.0557	0.0449	0.9937	0.9938	0.0399	0.0093	0.248	0.0150
SVMBotr	100	0.0443	0.0384	0.9960	0.9981	0.0382	0.0073	0.388	0.0150
MLPBotr	100	0.0327	0.0265	0.9978	0.9981	0.0239	0.0055	40.600	0.0400
DTBagr	150	0.1023	0.0871	0.9788	0.9788	0.0868	0.0176	0.320	0.0420
SVMBagr	150	0.0427	0.0365	0.9963	0.9979	0.0344	0.0070	1.923	0.0480
MLPBagr	150	0.0307	0.0233	0.9981	0.9981	0.0189	0.0051	69.295	0.2220
DTBotr	150	0.0525	0.0431	0.9944	0.9945	0.0393	0.0089	0.442	0.0300
SVMBotr	150	0.0443	0.0384	0.9960	0.9981	0.0382	0.0073	1.448	0.0300
MLPBotr	150	0.0327	0.0265	0.9978	0.9981	0.0239	0.0055	36.989	0.0310
DTBagr	200	0.1054	0.0897	0.9775	0.9775	0.0896	0.0180	0.366	0.0420
SVMBagr	200	0.0431	0.0370	0.9962	0.9980	0.0349	0.0071	0.574	0.0470
MLPBagr	200	0.0309	0.0235	0.9981	0.9981	0.0191	0.0052	91.345	0.1000
DTBotr	200	0.0519	0.0431	0.9945	0.9947	0.0398	0.0088	0.540	0.0380
SVMBotr	200	0.0443	0.0384	0.9960	0.9981	0.0382	0.0073	0.262	0.0090
MLPBotr	200	0.0327	0.0265	0.9978	0.9981	0.0239	0.0055	42.799	0.0450

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