# Research on a Time Series Data Prediction Model Based on Causal Feature Weight Adjustment
## 1 Introduction  
Time series data, also referred to as time-stamped data, are a sequence of data points indexed in time order. They are widely present in our daily lives, such as in weather forecasting, stock markets, and other fields. Nowadays, the accurate prediction and analysis of time series data have become a hot research topic in the field of artificial intelligence. Stock prices are typical time series data. Many scholars have carried out research on stock price forecasting due to its potential high returns. However, since stock data are often unstable and noisy and are greatly affected by international relations and national policies, the prediction of stock data has always been a challenging problem. Therefore, we take stock price data as an example to study the forecast of time series data, proposing a new method to improve prediction accuracy.  
Thanks to the extensive research scholars have conducted, many effective methods have been proposed. Before the advent of machine learning, statistical approaches were widely tried and tested. The Exponential Smoothing Model [1] uses the exponential window function to smooth time series data and then analyze them. The ARMA [2] is another popular technique for stock market analysis, which combines the Auto-Regressive (AR) model, which models the momentum and mean reversion effects observed in trading markets, and the Moving Average (MA) model, which tries to capture the shock effects observed. The ARIMA [3] is a natural extension of the ARMA model that can reduce a non-stationary series to a stationary series. Machine learning methods include random forest [4], xgboost [5], and support vector machine [6]. As neural network models show their great potential, more and more deep learning models have been proposed for stockprice prediction, based on RNN [7], CNN [8], LSTM [9], GRU [10], and so on. We will introduce some of them in the next chapter.  
We found that when using machine learning methods for time series prediction, the choice of features greatly affects the prediction performance. Existing methods often can only find correlations within variables. As we all know, correlation differs from causality, which is a more essential and thus more stable and reliable relationship in time series data. Therefore, it is of great significance to explore the causal relationship between stock price data for forecasting.  
To achieve this, we devised an effective novel method. First, we selected a set of stock factors as features and used a causal discovery algorithm to model the causal relationship between these features and the objective. Next, we adjusted the weights of the features that had a causal relationship with the objective. Finally, we used the weighted feature set as input to predict the stock price with the LSTM neural network.  
The contributions of this paper are as follows: (1) To the best of our knowledge, we apply the causal discovery algorithm based on a structural causal model to the stock price prediction for the first time. (2) Based on the LiNGAM algorithm and LSTM algorithm, we propose LWA-LSTM, which is capable of discovering causality and predicting time series data. (3) We find that there is a causal relationship rather than a correlation between many features and the stock price to be predicted. (4) LWA-LSTM achieved excellent performance when tested on real stock data. Our work validates the potential for adding causality and causal weight adjustment to produce more reliable and accurate predictions for such tasks.  
This paper is organized as follows. Section 2 introduces the relevant work. Section 3 provides a detailed description of the LWA-LSTM method we devised. In Section 4, we show the experiment settings and results. Finally, in Section 5, we summarize our work and look forward to the future.