Table 6.

A summary of the top forecasting methods used with each house to forecast the wake scores. Each house shows the machine learning algorithm that performed the best and the corresponding NMAE and NRMSE for each forecasting method. If the NMAE error was less than 2% we highlighted with blue, and if the NMAE error was between 2-4% we highlighted with yellow.

Side by Side Comparison of Results for Original Scores and Normalized Scores when Forecasting for Wake Scores
	Original Scores									Normalized Scores
	Independent Prediction			Cross Prediction			Joint Prediction			Independent Prediction			Cross Prediction			Joint Prediction
	algorithm	NMAE	NRMSE	algorithm	NMAE	NRMSE	algorithm	NMAE	NRMSE	algorithm	NMAE	NRMSE	algorithm	NMAE	NRMSE	algorithm	NMAE	NRMSE
HH101	svm	0.049	0.084	svm	0.049	0.084	svm	0.049	0.084	forest	9.48E-09	1.64E-08	forest	9.98E-09	1.56E-08	forest	9.60E-09	1.60E-08	HH101
HH102	svm	0.018	0.041	svm	0.018	0.041	svm	0.018	0.041	logistic	1.16E-09	2.23E-09	logistic	1.18E-09	2.50E-09	logistic	1.12E-09	2.41E-09	HH102
HH103	svm	0.017	0.116	svm	0.017	0.116	svm	0.017	0.116	logistic	2.91E-11	1.93E-10	logistic	2.85E-11	1.94E-10	logistic	2.88E-11	1.94E-10	HH103
HH104	forest	0.045	0.082	svm	0.048	0.083	forest	0.047	0.083	forest	3.03E-09	5.56E-09	linear	3.38E-09	5.72E-09	forest	3.11E-09	5.75E-09	HH104
HH105	svm	0.024	0.116	svm	0.024	0.116	svm	0.024	0.116	tree	8.89E-10	3.70E-09	logistic	9.30E-10	4.09E-09	tree	8.89E-10	3.63E-09	HH105
HH106	svm	0.057	0.139	svm	0.057	0.139	svm	0.057	0.139	logistic	6.59E-09	1.48E-08	logistic	7.09E-09	1.50E-08	logistic	6.47E-09	1.45E-08	HH106
HH108	svm	0.069	0.167	svm	0.069	0.167	svm	0.069	0.167	forest	2.61E-09	4.73E-09	tree	2.37E-09	4.68E-09	tree	2.47E-09	4.84E-09	HH108
HH109	svm	0.060	0.112	svm	0.060	0.112	svm	0.060	0.112	linear	1.91E-08	3.27E-08	svm	1.92E-08	3.27E-08	svm	1.95E-08	3.22E-08	HH109
HH110	forest	0.028	0.028	forest	0.023	0.023	logistic	0.029	0.031	forest	1.39E-09	1.54E-09	tree	4.27E-09	5.56E-09	forest	2.06E-09	2.72E-09	HH110
HH111	svm	0.025	0.042	svm	0.025	0.042	svm	0.025	0.042	forest	8.57E-10	1.40E-09	forest	9.19E-10	1.42E-09	forest	8.78E-10	1.46E-09	HH111
HH112	svm	0.045	0.075	svm	0.045	0.075	svm	0.045	0.075	forest	5.30E-09	8.55E-09	forest	5.70E-09	8.84E-09	forest	5.59E-09	8.73E-09	HH112
HH113	svm	0.030	0.097	svm	0.030	0.097	svm	0.030	0.097	svm	2.66E-09	7.05E-09	logistic	2.45E-09	7.23E-09	logistic	2.46E-09	7.13E-09	HH113
HH114	forest	0.100	0.145	forest	0.093	0.142	forest	0.098	0.153	forest	1.19E-08	1.69E-08	forest	1.12E-08	1.67E-08	forest	1.16E-08	1.68E-08	HH114
HH116	svm	0.023	0.080	svm	0.023	0.080	svm	0.023	0.080	tree	1.95E-09	5.88E-09	forest	1.87E-09	5.96E-09	forest	1.92E-09	5.70E-09	HH116
HH117	forest	0.008	0.011	forest	0.024	0.025	forest	0.019	0.023	tree	7.42E-09	1.05E-08	forest	8.35E-09	1.23E-08	forest	4.99E-09	7.00E-09	HH117
HH118	svm	0.044	0.055	svm	0.044	0.055	svm	0.044	0.055	forest	2.30E-09	2.73E-09	tree	2.23E-09	2.81E-09	tree	1.72E-09	2.23E-09	HH118
HH119	svm	0.046	0.069	svm	0.046	0.069	svm	0.046	0.069	forest	6.98E-09	9.96E-09	forest	6.99E-09	1.02E-08	forest	7.02E-09	1.00E-08	HH119
HH120	svm	0.044	0.068	svm	0.044	0.068	svm	0.044	0.068	forest	4.56E-10	6.96E-10	forest	5.05E-10	8.08E-10	tree	4.68E-10	6.68E-10	HH120
HH122	tree	0.017	0.021	logistic	0.027	0.028	tree	0.020	0.026	tree	1.38E-09	1.81E-09	forest	2.16E-10	2.63E-10	logistic	1.08E-09	1.37E-09	HH122
HH123	svm	0.045	0.137	svm	0.045	0.137	svm	0.045	0.137	logistic	7.73E-09	2.06E-08	tree	7.40E-09	2.02E-08	forest	7.51E-09	2.00E-08	HH123