# Research on a Time Series Data Prediction Model Based on Causal Feature Weight Adjustment
## 2 Related Work
This proposed method consists of two phases. First, it applied a piece-wise linear regression method (PLR) (which extracts long-term temporal features) and a CNN (which extracts short-term spatial market features) as a dual feature extraction method. Second, an encoder-decoder framework formed by an LSTM was applied to select and merge relevant features and then perform trend prediction. Among the multiple advantages of transformers, the ability to capture long-range dependencies and interactions is especially attractive for time series modeling, leading to exciting progress in various time series applications. By highlighting their advantages and limitations, [PERSON] et al. [20] comprehensively and systematically summarize transformers' work on the latest advances in time series data modeling. [PERSON] et al. [21] proposed a capsule network based on a transformer. They captured semantic features with a transformer encoder and text structure information with capsule networks, thereby extracting features from the text on social media for stock predictions. [PERSON] et al. [22] proposed a new deep learning method for time series prediction, SSDNet. This approach combines the transformer architecture with a state-space model to provide probabilistic and interpretable predictions. The paper evaluates the performance of SSDNet on five datasets, showing that SSDNet is an effective method in terms of accuracy and speed. [PERSON] et al. [23] proposed the StockNet model based on GRU with a new data augmentation approach to overcome the issue of overfitting. [PERSON] et al. [24] propose a deep learning-based hybrid model that consists of two well-known DNN architectures: LSTM and GRU. The approach involves passing the input data to the LSTM network to generate a first-level prediction and then passing the output of the LSTM layer to the GRU layer to obtain the final prediction. A novel deep-learning approach to predict the stock market using both historical stock prices and financial news data can be found in [PERSON] et al. [25]. In this study, mainly two novel approaches were used. First, a two-stream gated recurrent unit (TGRU) model for stock price trend forecasting; second, a sentiment Stock2 Vec embedding model associated with financial news data as well as a sentiment dictionary. The proposed network achieved a mean squared error (MSE) of 0.00098 in prediction, outperforming previous neural network approaches. [PERSON] et al. [26] proposed AutoAI for time series forecasting (AutoAI-TS). The model can use classical statistical models, machine learning (ML) models, and deep learning models to create prediction pipelines and use the T-Daub mechanism to select the best pipeline for prediction. Existing predictive models often only reveal the underlying correlation relationships rather than causal relationships between features and stock prices. A common causal analysis method for time series data is the Granger causality test proposed by [PERSON] [27], which promoted econometrics. [PERSON] and [PERSON] [28] tested the nonlinear causality between transaction volume and returns, confirming the validity of the Granger causality test. With the linear and non-linear Granger causality tests, [PERSON] et al. [29] found that there is a significant two-way causality between daily stock returns and trading volume in Korea. Using the same technique, [PERSON] et al. [30] found that the Michigan Consumer Sentiment Index has a causal relationship with the consumption trend in the United States. However, there is no solid causal theory foundation for the Granger causality test. It has been recognized that proving causality requires counterfactual reasoning. A causal relationship is more stable and does not change over time, which is of more interest to equity investors. Therefore, there is an urgent need to study the real causal relationship between stock factors. The existing literature shows that the research on stock price prediction based on causality among factors has not received enough attention. [PERSON] et al.