# Research on a Time Series Data Prediction Model Based on Causal Feature Weight Adjustment
## 2 Related Work  
For a long time, people have been disputing whether the stock market can be predicted. The efficient market hypothesis proposed by [PERSON] [11] holds that information is efficient; that is, new information can be quickly reflected in asset prices, so future stock prices cannot be predicted based on historical information. [PERSON] and [PERSON] [12] systematically investigated the empirical real-world out-of-sample performance of plain linear regressions to predict the equity premium and found that none of the popular variables worked. [PERSON], [PERSON], and [PERSON] [13] reexamined whether 29 variables from 26 papers published after [PERSON] and [PERSON], as well as the original 17 variables, were useful in predicting the equity premium in-sample and out-of-sample. The results show that the predictive performance of popular variables is still disappointing. However, because the efficient market hypothesis has very strong assumptions, it is difficult to satisfy in the real world. With the development of computer technology, various forecasting methods other than linear regression have been enriched. Therefore, many researchers still make stock forecasts for high returns. Fundamental analysis and technical analysis have been used for decades. The traditional stock forecasting method is generally based on statistical models, establishing a linear model between stock features and stock price. [PERSON] et al. [14] built an ARIMA model using the monthly closing price of the SSE Composite Index to predict the closing price in three months, verifying the accuracy of the ARIMA model in short-term prediction. Such methods achieved good results with very few model parameters and low computational complexity. However, they often assume data with linearity, stationarity, and normality, which is often too strict. There are many complex nonlinear relationships between stock variables. Thanks to the massive data recorded in the financial market, the use of machine learning in stock markets is growing rapidly. Various algorithms such as support vector machines, perceptrons, artificial neural networks, and decision trees have been applied to stock price prediction to improve the accuracy. [PERSON] [15] applied a support vector machine to stock prediction, and experiments showed that the method outperformed traditional neural networks. [PERSON] [16] used artificial neural networks combined with global search techniques (GA/SA) to make predictions. Although these methods have been greatly improved compared with the traditional methods, complex feature engineering and poor model scalability have always troubled researchers. Deep learning has now become the most popular solution for most AI problems. Many researchers use RNNs to make stock price predictions. Although Recurrent Neural Networks (RNNs) possess internal memory and feedback connections, making them capable of handling sequences of arbitrary length, the model's performance is heavily compromised as the input length increases. Furthermore, excessively long inputs can lead to issues of vanishing or exploding gradients, making the training process extremely challenging. The LSTM (Long Short-Term Memory) model was developed based on the RNN. LSTM incorporates three control units: the forget gate, the input gate, and the output gate. These units effectively enhance the model's ability to handle long-range dependencies in sequential data. In addition to approximating complex non-linear relationships, LSTM also offers advantages such as high accuracy, strong learning capability, robustness, and fault tolerance. [PERSON] [17] designed stock forecasting models based on LSTM and CNN, respectively, and built a stock trading strategy with prediction results. [PERSON] et al. [18] proposed three stock prediction models based on the CNN, RNN, and LSTM respectively, and compared their performance by predicting the stock price of listed companies. [PERSON] et al. [19] proposed a stock price trend prediction model (TPM) based on the encoder-decoder mechanism.