## 3 Materials and Method
### Data Preprocessing and Augmentation  
In time series modeling for complex systems, data preprocessing and augmentation are critical steps for building high-quality signal prediction models. Raw data often exhibit issues such as temporal misalignment, missing values, and non-stationarity, all of which can \begin{table}
\begin{tabular}{l l c} \hline \hline
**Data Type** & **Source** & **Sample Size** \\ \hline Stock Trading Data & Wind/Tushare & 300 stocks \(\times\) 2400 days \\ Technical Indicators & Self-calculated & 300 stocks \(\times\) 13 \(\times\) 2400 days \\ Asset Graph Matrix & Constructed & 300 \(\times\) 300 \(\times\) 2400 graphs \\ Macroeconomic Indicators & Wind & 120 time points per indicator \\ Policy News Summaries & Caixin, NBS, Sina Finance & 21,483 texts \\ Language Model Output & ChatGPT API & One-to-one with policy texts \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data types, sources, and sample statistics used in this study. significantly impair model performance and generalization if left unaddressed. Therefore, it is essential to standardize and reconstruct time series data through systematic signal processing techniques, while also leveraging augmentation strategies to enhance robustness against structural shifts and local perturbations. First, for multi-source asynchronous data, temporal alignment must be achieved by unifying the time base. Common approaches include interpolation-based synchronization and time-window resampling. When handling missing values, if the data exhibit strong continuity, a forward fill strategy can be used, where the last observed value is carried forward to fill in the missing point, defined as  
\[x_{t}= \begin{cases}x_{t},&\text{if $x_{t}$ is observed}\\ x_{t-1},&\text{if $x_{t}$ is missing}\end{cases}  \tag{9}\]  
For more stationary signals, mean imputation can be employed, where missing values are replaced by the average of historical observations:  
\[x_{t}=\frac{1}{N}\sum_{i=1}^{N}x_{i} \tag{10}\]  
In terms of feature construction, statistical functions and filtering operators based on time series are introduced to enhance the model's ability to detect trends and local patterns. For instance, the Relative Strength Index (RSI) measures the strength of upward or downward movements over a short horizon and is calculated as  
\[\text{RSI}=100-\frac{100}{1+\frac{\overline{\xi}}{L}}, \tag{11}\]  
Here, \(\overline{\xi}\) denotes the average gain over a given period, and \(\overline{L}\) the average loss. Another widely used feature is the Moving Average Convergence Divergence (MACD), which reflects the difference between fast and slow exponential moving averages (EMAs):  
\[\text{MACD}_{t}=\text{EMA}_{\text{fast}}(x_{t})-\text{EMA}_{\text{slow}}(x_{t}) \tag{12}\]  
The EMA itself is computed as  
\[\text{EMA}_{t}=\alpha x_{t}+(1-\alpha)\text{EMA}_{t-1}, \tag{13}\]  
where \(\alpha=\frac{2}{n+1}\) is a smoothing factor related to the window size \(n\). Additionally, momentum-based features like On-Balance Volume (OBV) can be incorporated to reflect both the direction and intensity of sequence fluctuations. To further improve the model's ability to recognize patterns under various conditions, a diverse set of data augmentation techniques is employed. The most fundamental strategy is sliding window sampling, where overlapping samples are constructed with a fixed step size to capture local temporal dynamics. Sequence reversal is another augmentation method that simulates symmetric perturbations, allowing evaluation of the model's sensitivity to temporal order by generating sequences like \(\{x_{7},\ldots,x_{1}\}\). A more sophisticated method is event-driven contrastive sample generation, where key segments are extracted based on specific behavioral triggers (e.g., spikes, inflection points, or high-volatility regions), and used to form positive and negative sample pairs. This enables the model to differentiate between structurally similar yet semantically distinct patterns in the feature space.