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 15 Bagging and boosting ensemble regressors error metrics result over NYSE 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	0.0438	0.0327	0.9024	0.9024	0.0244	0.0218	0.004	0.002
SVMBagr	1	0.0463	0.0358	0.8908	0.8918	0.0258	0.0228	0.004	0.002
MLPBagr	1	0.0135	0.0115	0.9907	0.9948	0.0111	0.0066	0.131	0.003
DTBotr	1	0.0457	0.0369	0.8937	0.8937	0.0320	0.0227	0.004	0.001
SVMBotr	1	0.0463	0.0358	0.8908	0.8918	0.0258	0.0228	0.004	0.001
MLPBotr	1	0.0089	0.0064	0.9959	0.9965	0.0048	0.0043	0.152	0.001
DTBagr	5	0.0283	0.0233	0.9591	0.9593	0.0201	0.0142	0.011	0.002
SVMBagr	5	0.0456	0.0354	0.8940	0.8959	0.0249	0.0224	0.010	0.001
MLPBagr	5	0.0083	0.0062	0.9965	0.9981	0.0048	0.0041	2.160	0.002
DTBotr	5	0.0193	0.0145	0.9810	0.9811	0.0114	0.0093	0.012	0.002
SVMBotr	5	0.0465	0.0361	0.8901	0.8918	0.0253	0.0228	0.018	0.002
MLPBotr	5	0.0070	0.0047	0.9975	0.9981	0.0032	0.0034	0.491	0.002
DTBagr	15	0.0295	0.0250	0.9555	0.9556	0.0233	0.0149	0.029	0.007
SVMBagr	15	0.0465	0.0361	0.8899	0.8922	0.0256	0.0228	0.025	0.009
MLPBagr	15	0.0088	0.0068	0.9960	0.9984	0.0055	0.0043	3.590	0.020
DTBotr	15	0.0193	0.0153	0.9810	0.9811	0.0127	0.0093	0.093	0.009
SVMBotr	15	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	0.145	0.029
MLPBotr	15	0.0075	0.0055	0.9971	0.9984	0.0042	0.0037	2.557	0.007
DTBagr	20	0.0268	0.0235	0.9634	0.9635	0.0224	0.0134	0.036	0.006
SVMBagr	20	0.0465	0.0361	0.8900	0.8920	0.0253	0.0228	0.033	0.007
MLPBagr	20	0.0091	0.0072	0.9958	0.9984	0.0057	0.0044	3.767	0.011
DTBotr	20	0.0179	0.0139	0.9837	0.9837	0.0116	0.0085	0.041	0.005
SVMBotr	20	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	0.045	0.005
MLPBotr	20	0.0076	0.0055	0.9971	0.9984	0.0041	0.0037	3.305	0.009
DTBagr	50	0.0223	0.0189	0.9747	0.9747	0.0175	0.0110	0.084	0.009
SVMBagr	50	0.0465	0.0361	0.8900	0.8924	0.0256	0.0228	0.078	0.023
MLPBagr	50	0.0094	0.0076	0.9955	0.9983	0.0065	0.0046	8.237	0.022
DTBotr	50	0.0167	0.0138	0.9858	0.9858	0.0123	0.0081	0.111	0.010
SVMBotr	50	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	0.148	0.013
MLPBotr	50	0.0079	0.0058	0.9968	0.9982	0.0044	0.0039	6.851	0.014
DTBagr	100	0.0217	0.0187	0.9759	0.9759	0.0180	0.0108	0.150	0.016
SVMBagr	100	0.0465	0.0361	0.8900	0.8924	0.0255	0.0228	0.155	0.023
MLPBagr	100	0.0094	0.0076	0.9955	0.9984	0.0063	0.0046	19.791	0.058
DTBotr	100	0.0127	0.0107	0.9918	0.9918	0.0100	0.0062	0.424	0.026
SVMBotr	100	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	0.291	0.069
MLPBotr	100	0.0079	0.0058	0.9968	0.9982	0.0044	0.0039	6.788	0.015
DTBagr	150	0.0214	0.0188	0.9767	0.9767	0.0183	0.0105	0.271	0.058
SVMBagr	150	0.0464	0.0361	0.8903	0.8926	0.0255	0.0227	0.169	0.035
MLPBagr	150	0.0095	0.0077	0.9954	0.9983	0.0064	0.0046	24.502	0.071
DTBotr	150	0.0118	0.0098	0.9929	0.9929	0.0087	0.0057	0.502	0.044
SVMBotr	150	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	1.018	0.174
MLPBotr	150	0.0079	0.0058	0.9968	0.9982	0.0044	0.0039	6.617	0.015
DTBagr	200	0.0222	0.0195	0.9750	0.9750	0.0188	0.0108	0.860	0.378
SVMBagr	200	0.0465	0.0361	0.8900	0.8925	0.0257	0.0227	1.091	0.064
MLPBagr	200	0.0095	0.0076	0.9954	0.9983	0.0063	0.0046	35.916	0.092
DTBotr	200	0.0109	0.0090	0.9939	0.9939	0.0080	0.0053	0.521	0.063
SVMBotr	200	0.0455	0.0354	0.8947	0.8970	0.0252	0.0223	0.545	0.042
MLPBotr	200	0.0079	0.0058	0.9968	0.9982	0.0044	0.0039	4.847	0.068

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