Search

LSTM (long short-term memory) networks have been proven effective in processing stock data. However, the stability of LSTM is poor, it is greatly affected by data fluctuations, and it is weak in capturing long-term dependencies in sequential data. BiLSTM (bidirectional LSTM) has alleviated this issue to some extent; however, due to the inefficiency of information transmission within the LSTM units themselves, the generalization performance and accuracy of BiLSTM is still not very satisfactory. To address this problem, this paper improves LSTM units on the basis of traditional BiLSTM and proposes a He-BiLSTM (heterogeneous bidirectional LSTM) with a corresponding backpropagation algorithm. The parameters in He-BiLSTM are updated using the Adam gradient descent method. Experimental results show that compared to BiLSTM, He-BiLSTM has further improved in terms of accuracy, robustness, and generalization performance.

Long Short-Term Memory (LSTM) is an effective method for stock price prediction. However, due to the nonlinear and highly random nature of stock price fluctuations over time, LSTM exhibits poor stability and is prone to overfitting, resulting in low prediction accuracy. To address this issue, this paper proposes a novel variant of LSTM that couples the forget gate and input gate in the LSTM structure, and adds a “simple” forget gate to the long-term cell state. In order to enhance the generalization ability and robustness of the variant LSTM, the paper introduces an attention mechanism and combines it with the variant LSTM, presenting the Attention Mechanism Variant LSTM (AMV-LSTM) model along with the corresponding backpropagation algorithm. The parameters in AMV-LSTM are updated using the Adam gradient descent method. Experimental results demonstrate that the variant LSTM alleviates the instability and overfitting issues of LSTM, effectively improving prediction accuracy. AMV-LSTM further enhances accuracy compared to the variant LSTM, and compared to AM-LSTM, it exhibits superior generalization ability, accuracy, and convergence capability.

A novel and highly efficient Pd-catalyzed cross-coupling of triarylbismuths with a variety of P(O)--H compounds has been developed that proceeds smoothly without exclusion of moisture or air and provides a general and powerful tool for the preparation of various valuable arylphosphonates, arylphosphinates, and arylphosphine oxides, with high atom-economy, operational simplicity of the procedure, and good to high yield. The coupling reaction is the first example of transition-metal-catalyzed C--P bond construction using triarylbismuth compounds as substrates. DFT calculations reveal that C--P bond formation is the rate-determing step. [ABSTRACT FROM AUTHOR]