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Note: ( a – c <t>)</t> <t>forecasting</t> results for the SZSE, the HSI, and the SSE, respectively. The blue lines represent the actual data trend, and the orange lines indicate the forecasted data trend; on the left side, <t>RNN</t> represents p ^ R N N , LSTM represents p ^ L S T M , RNN+LSTM represents p ^ R N N + ε ^ L S T M , and AR-RNN-LSTM represents the hybrid forecasting results. R represents the regression coefficient.
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Note: ( a – c ) forecasting results for the SZSE, the HSI, and the SSE, respectively. The blue lines represent the actual data trend, and the orange lines indicate the forecasted data trend; on the left side, RNN represents p ^ R N N , LSTM represents p ^ L S T M , RNN+LSTM represents p ^ R N N + ε ^ L S T M , and AR-RNN-LSTM represents the hybrid forecasting results. R represents the regression coefficient.

Journal: PLOS One

Article Title: Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

doi: 10.1371/journal.pone.0322737

Figure Lengend Snippet: Note: ( a – c ) forecasting results for the SZSE, the HSI, and the SSE, respectively. The blue lines represent the actual data trend, and the orange lines indicate the forecasted data trend; on the left side, RNN represents p ^ R N N , LSTM represents p ^ L S T M , RNN+LSTM represents p ^ R N N + ε ^ L S T M , and AR-RNN-LSTM represents the hybrid forecasting results. R represents the regression coefficient.

Article Snippet: Pawar, Jalem and Tiwari used an RNN to make forecasting on stock prices of Apple Inc., and the forecasting accuracy exceeded 95%, with a loss close to 0.1% [ ].

Techniques:

Note: In the figure, the numeral appended to “MAE” or “MAD” indicates the size of the training set. Specifically, MAE200 represents the MAE value generated in forecasting the SZSE using ARMA-RNN-LSTM hybrid model when the training set size is 200.

Journal: PLOS One

Article Title: Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

doi: 10.1371/journal.pone.0322737

Figure Lengend Snippet: Note: In the figure, the numeral appended to “MAE” or “MAD” indicates the size of the training set. Specifically, MAE200 represents the MAE value generated in forecasting the SZSE using ARMA-RNN-LSTM hybrid model when the training set size is 200.

Article Snippet: Pawar, Jalem and Tiwari used an RNN to make forecasting on stock prices of Apple Inc., and the forecasting accuracy exceeded 95%, with a loss close to 0.1% [ ].

Techniques: Generated

Note: In the graph, the numeral appended to “MAE” or “MAD” indicates the size of training set. Specifically, MAE200 represents the MAE value generated in forecasting the HSI using ARMA-RNN-LSTM hybrid model when the training set size is 200.

Journal: PLOS One

Article Title: Enhanced separation of long-term memory from short-term memory on top of LSTM: Neural network-based stock index forecasting

doi: 10.1371/journal.pone.0322737

Figure Lengend Snippet: Note: In the graph, the numeral appended to “MAE” or “MAD” indicates the size of training set. Specifically, MAE200 represents the MAE value generated in forecasting the HSI using ARMA-RNN-LSTM hybrid model when the training set size is 200.

Article Snippet: Pawar, Jalem and Tiwari used an RNN to make forecasting on stock prices of Apple Inc., and the forecasting accuracy exceeded 95%, with a loss close to 0.1% [ ].

Techniques: Generated