## 2 Related Works
### Anomaly Detection and Pseudo-Labeling in Time-Series Data Analysis  
Anomaly detection in time-series data is a promising research topic in various fields, particularly in complex environments such as smart cities, industrial automation, and finance. In these fields, early detection and responses to abnormal data patterns are essential for maintaining system integrity and performance. Recent studies have proposed various techniques to efficiently handle the complexity and scale of time-series data. For example, research utilizing generative adversarial networks (GANs) has proven effective in detecting anomalies in multivariate time-series data [24]. These studies leverage GANs to learn the distribution of normal data and assess whether new data deviate from this distribution, thereby identifying anomalies. This approach is especially useful in high-dimensional datasets and has demonstrated detection accuracy superior to traditional statistical methods and simpler machine learning techniques. Deep learning techniques such as autoencoders have also gained attention for anomaly detection. Autoencoders learn to compress and reconstruct input data, detecting anomalies based on significant reconstruction errors when compared to normal data [25]. This method is particularly adept at capturing complex patterns in data, showing promise in various domains. Additionally, recent research has introduced transformer models for time-series anomaly detection [26]. Transformers are capable of effectively learning long-term dependencies in time-series data, leading to the development of more sophisticated anomaly detection models. This approach has been particularly beneficial in enhancing detection performance in multidimensional time-series data. These studies highlight the importance of anomaly detection in time-series data and demonstrate the effective application of various techniques in real-world scenarios. Building on these advancements, our study integrates pseudo-labeling into the semantic segmentation of time-series data, enabling more precise and efficient analysis in complex environments such as smart cities. Pseudo-labeling is a semi-supervised learning technique that has gained significant attention in recent years, particularly in scenarios where labeled data are scarce or expensive to obtain. This approach involves training a machine learning model on a small set of labeled data and then using the model to predict the labels for an unlabeled dataset. The most confident predictions are then added to the labeled set, and the model is retrained iteratively. This method leverages the vast amounts of unlabeled data typically available, improving the model's performance by expanding the training dataset without extensive manual labeling. In the context of time-series data, pseudo-labeling has proven effective for various tasks, such as anomaly detection, fault diagnosis, and predictive maintenance [27]. In the field of smart agriculture, pseudo-labeling has been employed to automatically label environmental sensor data, facilitating real-time decision-making processes, including irrigation management and crop monitoring. In industrial settings, pseudo-labeling techniques have been utilized to identify patterns in mechanical operational data, enabling the early detection of equipment failures and the optimization of maintenance schedules [28]. Recent studies have further emphasized the potential of pseudo-labeling in improving the scalability and accuracy of models, particularly for large-scale time-series datasets. Pseudo-labeling reduces reliance on fully manually labeled datasets, which is advantageous in domains where manual labeling is not only time-consuming but also susceptible to human error. For example, [PERSON] et al. [29] demonstrated the efficacy of a multi-stage learning strategy using pseudo-labeling for time-series anomaly detection, significantly reducing the need for manual labeling while enhancing model accuracy. Similarly, [PERSON] et al.