Abstract:
Abstract： The stock market is crucial for economic development， according to its intense volatility， investors can effectively reduce investment risks and achieve higher returns if they can predict changes in stock prices more accurately. Due to the limitations of traditional time series models such as ARIMA in dealing with nonlinear problems， its forecasting effect is often unsatisfactory in the stock market. This paper proposes an innovative hybrid algorithm， which combines CNN-BiLSTM and LightGBM technology with dual attention mechanism， and makes use of the powerful nonlinear learning ability of neural network to achieve efficient and accurate prediction of stock market volatility. In practice， the stock data is preprocessed by ARIMA model， and then convolutional neural network combined with attention mechanism is used to construct feature attention module and extracts the key features from the stock data in an adaptive way. Then， by integrating bidirectional long short-term memory network and the attention mechanism， a temporal attention module is constructed to make a preliminary prediction of the future trend of stock prices. Finally， in order to further optimize the prediction accuracy， the model is introduced LightGBM to construct an error correction module to finely adjust the preliminary prediction results. The experiments show that the proposed model can not only improve the prediction accuracy， but also provide strong decision support for investors and institutions， so that they can more keenly explore market opportunities and achieve the goal of maximizing profits.

Key words: Key words： bidirectional long short-term memory network, LightGBM, attention mechanism, convolutional neural network, stock prediction