Table 8.

Time series forecasting results for IMS datasets.

Dataset	Metric	CNN [15]	Deep RNNs [38]	LSTM [17]	Transformer [31]	Informer
Set1	MAE	$5.2431\times {10}^{-2}$	$5.4199\times {10}^{-2}$	$5.6683\times {10}^{-2}$	$5.3548\times {10}^{-2}$	$5.2303\times {10}^{-2}$
	MSE	$4.7943\times {10}^{-3}$	$5.1087\times {10}^{-3}$	$5.5564\times {10}^{-3}$	$4.9876\times {10}^{-3}$	$4.7844\times {10}^{-3}$
	RMSE	$6.9241\times {10}^{-2}$	$7.1475\times {10}^{-2}$	$7.4541\times {10}^{-2}$	$7.0623\times {10}^{-2}$	$6.9169\times {10}^{-2}$
	Running Time (s)	480	600	400	4451	176
Set2	MAE	$4.8317\times {10}^{-2}$	$4.9114\times {10}^{-2}$	$5.3561\times {10}^{-2}$	$5.1860\times {10}^{-2}$	$4.8588\times {10}^{-2}$
	MSE	$3.8702\times {10}^{-3}$	$3.9699\times {10}^{-3}$	$4.7065\times {10}^{-3}$	$4.4446\times {10}^{-3}$	$3.9107\times {10}^{-3}$
	RMSE	$6.2211\times {10}^{-2}$	$6.3007\times {10}^{-2}$	$6.8604\times {10}^{-2}$	$6.6669\times {10}^{-2}$	$6.2536\times {10}^{-2}$
	Running Time (s)	480	750	800	5999	177
Set3	MAE	$4.8944\times {10}^{-2}$	$5.2173\times {10}^{-2}$	$5.0191\times {10}^{-2}$	$5.0256\times {10}^{-2}$	$4.7281\times {10}^{-2}$
	MSE	$3.8858\times {10}^{-3}$	$4.4246\times {10}^{-3}$	$4.0997\times {10}^{-3}$	$4.0718\times {10}^{-3}$	$3.9268\times {10}^{-3}$
	RMSE	$6.2336\times {10}^{-2}$	$6.6518\times {10}^{-2}$	$6.4029\times {10}^{-2}$	$6.3811\times {10}^{-2}$	$6.2664\times {10}^{-2}$
	Running Time (s)	480	825	800	5578	164