Journal of Big Data

Table 11 Results of JFK airport

From: Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

Algorithm		Time difference: 2 h						Time difference: 4 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.787	0.819	0.751	0.783	0.055	0.637	0.790	0.827	0.745	0.784	0.049	0.637
	Delayed		0.759	0.826	0.791				0.758	0.837	0.795
RF	Normal	0.843	0.835	0.864	0.849	0.993	21.019	0.850	0.838	0.877	0.857	1.058	20.064
	Delayed		0.852	0.821	0.836				0.864	0.822	0.842
SVM	Normal	0.650	0.643	0.709	0.675	5.253	618.153	0.638	0.646	0.650	0.648	4.914	632.166
	Delayed		0.658	0.588	0.621				0.630	0.626	0.628
KNN	Normal	0.712	0.749	0.659	0.701	0.008	70.382	0.722	0.761	0.667	0.711	0.005	40.045
	Delayed		0.682	0.768	0.722				0.691	0.780	0.732
LR	Normal	0.581	0.597	0.560	0.578	0.107	0.637	0.573	0.594	0.527	0.558	0.118	0.637
	Delayed		0.566	0.603	0.584				0.556	0.622	0.587
XGB	Normal	0.779	0.783	0.785	0.784	0.164	2.548	0.769	0.772	0.778	0.775	0.127	2.548
	Delayed		0.774	0.772	0.773				0.765	0.760	0.762
LSTM	Normal	0.852	0.831	0.882	0.856	560.0	4.140	0.829	0.826	0.833	0.829	564.4	4.140
	Delayed		0.876	0.822	0.848				0.833	0.826	0.830

Algorithm		Time difference: 8 h						Time difference: 16 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.796	0.826	0.764	0.793	0.049	0.637	0.800	0.827	0.770	0.798	0.051	0.637
	Delayed		0.770	0.831	0.799				0.775	0.831	0.802
RF	Normal	0.843	0.835	0.865	0.850	1.018	20.382	0.840	0.832	0.863	0.847	1.091	20.064
	Delayed		0.853	0.820	0.836				0.850	0.817	0.833
SVM	Normal	0.643	0.656	0.635	0.646	5.503	766.242	0.642	0.661	0.618	0.639	6.148	802.548
	Delayed		0.630	0.651	0.640				0.625	0.667	0.645
KNN	Normal	0.724	0.763	0.669	0.713	0.011	48.726	0.725	0.758	0.681	0.717	0.008	48.726
	Delayed		0.692	0.782	0.735				0.697	0.771	0.733
LR	Normal	0.594	0.617	0.545	0.579	0.112	0.637	0.582	0.605	0.529	0.564	0.124	0.637
	Delayed		0.575	0.645	0.608				0.563	0.638	0.598
XGB	Normal	0.776	0.785	0.776	0.780	0.132	2.548	0.778	0.783	0.782	0.783	0.121	2.548
	Delayed		0.767	0.777	0.772				0.772	0.773	0.772
LSTM	Normal	0.814	0.829	0.790	0.809	565.3	4.140	0.799	0.773	0.843	0.807	565.1	4.140
	Delayed		0.802	0.838	0.820				0.829	0.756	0.791

Algorithm		Time difference: 24 h						Time difference 48 h
Algorithm		Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)	Accuracy	Precision	Recall	F1-score	Train (s)	Test (us)
DT	Normal	0.779	0.816	0.733	0.772	0.056	0.637	0.784	0.821	0.740	0.779	0.051	0.955
	Delayed		0.747	0.826	0.785				0.753	0.831	0.790
RF	Normal	0.837	0.822	0.869	0.845	1.081	21.975	0.846	0.830	0.880	0.854	1.067	20.064
	Delayed		0.854	0.803	0.827				0.865	0.811	0.837
SVM	Normal	0.618	0.637	0.589	0.612	6.033	824.841	0.625	0.641	0.608	0.624	6.403	890.127
	Delayed		0.601	0.649	0.624				0.610	0.644	0.626
KNN	Normal	0.723	0.755	0.678	0.714	0.008	51.911	0.721	0.752	0.681	0.714	0.011	43.949
	Delayed		0.695	0.770	0.730				0.695	0.764	0.728
LR	Normal	0.562	0.593	0.463	0.520	0.125	0.637	0.565	0.588	0.504	0.543	0.112	0.637
	Delayed		0.542	0.666	0.598				0.547	0.629	0.585
XGB	Normal	0.778	0.787	0.777	0.782	0.121	2.229	0.773	0.777	0.782	0.779	0.123	2.548
	Delayed		0.769	0.779	0.774				0.770	0.764	0.767
LSTM	Normal	0.778	0.780	0.771	0.776	568.2	4.140	0.736	0.724	0.761	0.742	569.4	4.140
	Delayed		0.776	0.785	0.780				0.748	0.710	0.729

Bold values indicate the greatest results

Back to article page