Table 4 Results of one-step-ahead... | Emerald Publishing

Table 4

Results of one-step-ahead forecasting for Jiuzhaigou

Period	Pre-COVID-19			Post-COVID-19
Measure	MAE	MAPE	RMSE	MAE	MAPE	RMSE
MGASF–CNN–LSTM	569.605	0.059	2499.107	800.075	0.297	2723.166
MGADF–CNN–LSTM	572.295	0.059	2509.339	796.016	0.255	2556.829
MMTF–CNN–LSTM	569.212	0.057	2497.596	816.137	0.462	2804.063
MRP–CNN–LSTM	569.057	0.055	2495.883	772.264	0.203	2553.734
GASF–CNN–LSTM	5080.788	2.127	7458.790	5489.8581	2.727	7320.211
GADF–CNN–LSTM	4987.968	2.093	7457.710	5630.6738	2.910	7504.604
MTF–CNN–LSTM	5249.359	2.232	7504.604	5646.3492	3.025	7531.272
RP–CNN–LSTM	5167.184	2.195	7501.466	5599.9146	2.901	7457.710
ARIMA	6527.507	12.243	8208.974	6687.443	14.430	8465.265
Holt–Winters	6530.087	13.456	8236.313	6838.233	15.200	8550.719
OLS	1844.708	0.509	2869.429	2396.440	0.814	3543.890
KNN	1381.840	0.471	2816.723	1943.539	0.540	2922.243
XG-BOOST	1374.348	0.467	2812.333	1933.859	0.517	2871.385

Period	Pre-COVID-19			Post-COVID-19
Measure	MAE	MAPE	RMSE	MAE	MAPE	RMSE
MGASF–CNN–LSTM	569.605	0.059	2499.107	800.075	0.297	2723.166
MGADF–CNN–LSTM	572.295	0.059	2509.339	796.016	0.255	2556.829
MMTF–CNN–LSTM	569.212	0.057	2497.596	816.137	0.462	2804.063
MRP–CNN–LSTM	569.057	0.055	2495.883	772.264	0.203	2553.734
GASF–CNN–LSTM	5080.788	2.127	7458.790	5489.8581	2.727	7320.211
GADF–CNN–LSTM	4987.968	2.093	7457.710	5630.6738	2.910	7504.604
MTF–CNN–LSTM	5249.359	2.232	7504.604	5646.3492	3.025	7531.272
RP–CNN–LSTM	5167.184	2.195	7501.466	5599.9146	2.901	7457.710
ARIMA	6527.507	12.243	8208.974	6687.443	14.430	8465.265
Holt–Winters	6530.087	13.456	8236.313	6838.233	15.200	8550.719
OLS	1844.708	0.509	2869.429	2396.440	0.814	3543.890
KNN	1381.840	0.471	2816.723	1943.539	0.540	2922.243
XG-BOOST	1374.348	0.467	2812.333	1933.859	0.517	2871.385

Source(s): Authors’ own work