Table 6 Results of one-step-ahead... | Emerald Publishing

Table 6

Results of one-step-ahead forecasting for Mount Siguniang

Period	Pre-COVID-19			Post-COVID-19
Measure	MAE	MAPE	RMSE	MAE	MAPE	RMSE
MGASF–CNN–LSTM	210.976	0.242	816.533	337.048	1.323	1162.448
MGADF–CNN–LSTM	213.156	0.268	898.192	331.583	0.886	911.618
MMTF–CNN–LSTM	212.489	0.250	880.895	334.897	1.291	1056.314
MRP–CNN–LSTM	210.006	0.226	765.240	327.739	0.617	903.387
GASF–CNN–LSTM	1119.225	1.819	1953.703	1335.785	3.320	2722.238
GADF–CNN–LSTM	1247.142	2.054	2195.241	1632.381	3.927	3340.243
MTF–CNN–LSTM	1221.156	2.014	2014.321	1326.100	2.433	2357.950
RP–CNN–LSTM	1239.115	2.028	2172.926	1595.881	3.838	3264.474
ARIMA	2274.267	3.941	3250.282	2642.256	9.400	4374.119
Holt–Winters	2287.577	4.020	3254.243	2653.593	9.610	4410.260
OLS	622.386	1.618	1514.051	838.636	1.787	1527.959
KNN	461.517	1.333	1188.725	522.277	1.355	1494.061
XG-BOOST	478.059	1.339	1305.557	535.810	1.418	1504.567

Period	Pre-COVID-19			Post-COVID-19
Measure	MAE	MAPE	RMSE	MAE	MAPE	RMSE
MGASF–CNN–LSTM	210.976	0.242	816.533	337.048	1.323	1162.448
MGADF–CNN–LSTM	213.156	0.268	898.192	331.583	0.886	911.618
MMTF–CNN–LSTM	212.489	0.250	880.895	334.897	1.291	1056.314
MRP–CNN–LSTM	210.006	0.226	765.240	327.739	0.617	903.387
GASF–CNN–LSTM	1119.225	1.819	1953.703	1335.785	3.320	2722.238
GADF–CNN–LSTM	1247.142	2.054	2195.241	1632.381	3.927	3340.243
MTF–CNN–LSTM	1221.156	2.014	2014.321	1326.100	2.433	2357.950
RP–CNN–LSTM	1239.115	2.028	2172.926	1595.881	3.838	3264.474
ARIMA	2274.267	3.941	3250.282	2642.256	9.400	4374.119
Holt–Winters	2287.577	4.020	3254.243	2653.593	9.610	4410.260
OLS	622.386	1.618	1514.051	838.636	1.787	1527.959
KNN	461.517	1.333	1188.725	522.277	1.355	1494.061
XG-BOOST	478.059	1.339	1305.557	535.810	1.418	1504.567

Source(s): Authors’ own work