## 2 Related Work
### Pseudo Anomaly Generation  
Synthetic data generation plays a crucial role in various domains for augmenting imbalanced datasets and ensuring privacy protection. It is well known that building robust anomaly detection models requires balanced datasets containing both normal and anomalous data [31]. However, to construct high-quality datasets, it is essential to secure relatively rare anomalous data. [PERSON] et al. [32] proposed a strategy to transform normal data into anomalous data using Cycle-GAN [33]. Cycle-GAN employs a training method that enables mutual transformation between images from different domains. This study utilized this characteristic to convert data into images and transform template data (normal data) into anomalies. The synthetic anomalies are then appended to the dataset for training detection models.  
[PERSON] et al. [34] introduced G2D, a GAN-based general framework for generating anomalies. This study categorizes synthetic data into three stages based on the learning progress of the generator: random samples, outliers surrounding the normal samples, and samples following the data distribution. Random samples and outliers are considered anomalies and used for training anomaly detection models. As a result, the detection models trained with G2D demonstrate stable performance as the proportion of outliers in the dataset increases.  
[PERSON] et al. [35] presented a novel method for unsupervised anomaly detection in industrial images. This method involves generating high-quality pseudo-anomaly images and enhancing normal image features. Experiments on various real datasets show performance improvements, specifically by enhancing normal image features to boost the model's prediction accuracy. The method also reduces the uncertainty of single models by integrating various anomaly scores through ensemble detection.  
[PERSON] et al. [36] introduced the FastLogAD system, designed for rapid anomaly detection in log data. It uses a mask-guided anomaly generation (MGAG) technique to generate pseudo-abnormal logs and a discriminative abnormality separation (DAS) model for efficient anomaly identification. FastLogAD significantly outperforms existing methods by achieving anomaly detection speeds at least ten times faster and competitive performance metrics, representing a significant advancement in log anomaly detection with an emphasis on speed and efficiency in handling security-related data.  
[PERSON] et al. [37] introduced AnomalyDiffusion, a new anomaly generation model that enables accurate anomaly detection with limited data. AnomalyDiffusion significantly improves generation accuracy and diversity by integrating anomaly location and appearance information. Experimental results demonstrate that this model outperforms existing methods in both generation accuracy and diversity, showing high performance in downstream anomaly detection tasks.  
While numerous studies have proposed methods for generating anomalous data, only a limited number of these focus on time-series data. Most synthetic anomaly generation techniques are confined to i.i.d data, such as images. In contrast, data collected from industrial processes often exhibit time-series characteristics, making i.i.d-based anomaly generation techniques unsuitable for generating time-series anomalous data.