Table 8.

Transformer in economics

Article	Aim of study	Specific approach	Benchmark methods for comparison	Superiority of the proposed method
(Wang & Li, 2022)	Detect renewable energy incidents from news articles containing accidents in various renewable energy systems	PTM word2vec, BERT, TCNN, TRNN	BERT-TCNNs BERT-TRNNs word2vec-TCNNs word2vec-TRNNs TCNNs, TRNNs	Effective and robust in detecting renewable energy incidents from large-scale textual materials
(Liao et al., 2021)	Predict multistep-ahead location marginal price	Transformer, with seq2seq architecture	LSTM, Bi-LSTM, GRU, Bi-GRU, and TCN	Avoid the error accumulation of the results, higher accuracy
(Yue et al., 2020)	Predict accurate energy and classify simultaneous status	BERT	GRU, LSTM, CNN	More stable and precise, higher prediction consistency
(Fisichella & Garolla, 2021)	Develop a complete trading system with a combination of trading rules on Forex time series data	ViT	ResNet50	Fewer computational resources to train

Note: PTM (Pre-Trained Model), BERT (Bidirectional Encoder Representation from Transformer), TRNN (Text Recursive Neural Network), TCNN (Text Convolution Neural Network), seq2seq (sequence to sequence), GRU (Gated Recurrent Unit), Bi-GRU (Bi-directional Gated Recurrent Unit), TCN (Train Communication Network), Vision Transformer (ViT).