Table 7.

AE in economics

Article	Aim of study	Specific approach	Benchmark methods for comparison	Superiority of the proposed method
(Wang et al., 2020a)	Forecast long-term time series in industrial production	SSAEN, SAEGN	BPNN, DLSTM, GrC-based long-term prediction model	Significantly improve the long-term time series prediction accuracy
(Long et al., 2020)	Learn features and recognize fault of Delta 3-D printers	SAE, ESN	ESN, SAE-Softmax, DBN-ESN	Best forecasting performance
(Heaton et al., 2017)	Predict and classify financial market	Stacked auto-encoders, DL	-	-
(Li et al., 2020a)	Detect feedwater heater performance	SDSAE	PCA(T2), PCA(SPE), GA-ELM, PCA-BPNN	Achieves the best performance according to detection threshold, computation accuracy, Accnormal, Accfault
(Suimon et al., 2020)	Represent the Japanese yield curve	AE	LSTM, VAR	Effective, and interpretable
(Ranjan et al., 2021)	Analyze and predict large-scale road network congestion	Convolutional AE	ConvLSTM, PredNet	More accurate prediction result, less trainable parameter and training time

Note: SSAEN (Stacked Sparse Auto-Encoders Network), SAEGN (Sparse Auto-Encoder Granulation Network), DLSTM (Deep Long Short-Term Memory), SAE (Sparse Autoencoder), ESN (Echo State Network), SDSAE (Sacked Denoising Sparse Autoencoder), SPE (Squared Prediction Error), GA-ELM (Genetic Algorithm based Extreme Learning Machine), VAR (Vector Autoregression), ConvLSTM (Convolution Long Short-Term memory), PredNet (Prediction Network)