Skip to main content

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

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

ModelsNo. of estimatorsRMSEMAER2EVSMedAERMSLETrain timeTest time
DTBagr10.20430.14260.91530.91540.09320.03720.0050.0010
SVMBagr10.06850.06410.99050.99830.06910.01210.0080.0020
MLPBagr10.03630.02770.99730.99730.02180.00610.3380.0010
DTBotr10.23440.19740.88850.88860.18820.04230.0040.0010
SVMBotr10.06810.06360.99060.99820.06760.01210.0180.0010
MLPBotr10.03390.02570.99770.99770.01920.00571.4490.0010
DTBagr50.09840.08090.98030.98030.07210.01750.0150.0020
SVMBagr50.05740.05260.99330.99820.05620.01010.0180.0010
MLPBagr50.03130.02400.99800.99800.01960.00533.0250.0030
DTBotr50.15540.11750.95100.95110.08270.03050.0140.0020
SVMBotr50.06100.05590.99250.99810.05900.01080.0430.0010
MLPBotr50.03240.02490.99790.99790.01950.00541.4670.0030
DTBagr150.10760.09040.97650.97650.08000.01940.0310.0020
SVMBagr150.04600.04060.99570.99810.04170.00770.0540.0050
MLPBagr150.02920.02210.99830.99830.01910.00497.0540.0070
DTBotr150.08200.06670.98640.98640.05910.01410.0420.0040
SVMBotr150.03890.03250.99690.99790.03000.00640.1700.0080
MLPBotr150.03210.02470.99790.99800.01990.00547.1740.0060
DTBagr200.10300.08770.97850.97850.08020.01840.0300.0030
SVMBagr200.04820.04300.99530.99820.04440.00810.0610.0060
MLPBagr200.02940.02230.99830.99830.01890.00499.4030.0190
DTBotr200.07460.05920.98870.98870.05000.01250.0710.0060
SVMBotr200.04430.03840.99600.99810.03820.00730.2020.0110
MLPBotr200.03190.02470.99790.99800.02030.005410.9100.0270
DTBagr500.09890.08110.98010.98010.07270.01740.1170.0100
SVMBagr500.04490.03910.99590.99800.03870.00740.2350.0190
MLPBagr500.03050.02320.99810.99810.01890.005127.0880.0210
DTBotr500.05680.04440.99340.99350.03680.00950.1330.0150
SVMBotr500.04430.03840.99600.99810.03820.00730.3570.0140
MLPBotr500.03220.02580.99790.99810.02290.005433.0040.0290
DTBagr1000.09880.08400.98020.98020.08360.01730.5370.0860
SVMBagr1000.04420.03820.99600.99800.03690.00731.2200.0340
MLPBagr1000.03030.02300.99810.99810.01880.005152.3940.0530
DTBotr1000.05570.04490.99370.99380.03990.00930.2480.0150
SVMBotr1000.04430.03840.99600.99810.03820.00730.3880.0150
MLPBotr1000.03270.02650.99780.99810.02390.005540.6000.0400
DTBagr1500.10230.08710.97880.97880.08680.01760.3200.0420
SVMBagr1500.04270.03650.99630.99790.03440.00701.9230.0480
MLPBagr1500.03070.02330.99810.99810.01890.005169.2950.2220
DTBotr1500.05250.04310.99440.99450.03930.00890.4420.0300
SVMBotr1500.04430.03840.99600.99810.03820.00731.4480.0300
MLPBotr1500.03270.02650.99780.99810.02390.005536.9890.0310
DTBagr2000.10540.08970.97750.97750.08960.01800.3660.0420
SVMBagr2000.04310.03700.99620.99800.03490.00710.5740.0470
MLPBagr2000.03090.02350.99810.99810.01910.005291.3450.1000
DTBotr2000.05190.04310.99450.99470.03980.00880.5400.0380
SVMBotr2000.04430.03840.99600.99810.03820.00730.2620.0090
MLPBotr2000.03270.02650.99780.99810.02390.005542.7990.0450