Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism

Table 1

Comparison of anomaly detection performance based on precision, recall, and F1-score.

Methods	Climb			Cruise			Descent
		R	F1		R	F1		R	F1
IF	0.5833	0.3293	0.4211	0.6333	0.7039	0.6667	0.5108	0.7055	0.5926
LSTMS	0.6944	0.9801	0.8195	0.4425	0.9440	0.6136	0.8240	0.6352	0.7769
LSTM-AE	0.8885	0.9426	0.9147	0.8768	0.9417	0.9134	0.7284	0.8534	0.7860
LSTM-VAE	0.7722	0.9443	0.8496	0.8119	0.9716	0.8961	0.8902	1.0	0.9419
VAE-based MHSA-LSTM	0.9145	0.9833	0.9503	0.8840	1.0	0.9384	0.9453	1.0	0.9718