A Deep Learning Prediction Model for Structural Deformation Based on Temporal Convolutional Networks

Table 3

Orthogonal experiment results.

Test number	Types and levels of factors					RMSE	MAPE	MAE	Running time (min)
	A	B	C	D	E
1	5	8	8	8	0.0001	1.08	1.13	0.66	7.73
2	5	16	16	12	0.001	1.05	0.86	0.53	38.73
3	5	24	32	16	0.01	2.26	5.61	1.70	97.90
4	5	32	64	20	0.05	9.09	9.93	7.26	247.30
5	6	8	16	16	0.05	1.08	1.07	0.58	45.34
6	6	16	8	20	0.01	1.10	0.84	0.57	47.70
7	6	24	64	8	0.001	1.05	0.64	0.49	75.61
8	6	32	32	12	0.0001	2.41	1.83	1.69	87.78
9	7	8	32	20	0.001	1.22	0.63	0.47	77.55
10	7	16	64	16	0.0001	1.20	1.47	0.74	129.25
11	7	24	8	12	0.05	1.17	1.73	0.74	36.68
12	7	32	16	8	0.01	1.08	0.76	0.51	39.23
13	8	8	64	12	0.01	1.08	0.89	0.57	68.86
14	8	16	32	8	0.05	0.98	0.34	0.41	41.04
15	8	24	16	20	0.0001	1.18	1.5648	0.73	98.53
16	8	32	8	16	0.001	1.28	1.3177	0.83	63.74

The bold values represent the best prediction result of the model when the TCN model takes this set of parameters.