## 2 Related Work
(2020) applied various deep learning models for anomaly detection and time series forecasting and introduced a statistical method to overcome the limitations of high-dimensional time series data. [PERSON] et al. (2019) introduced the pioneering MCN-LSTM technique for real-time water quality monitoring, addressing the challenges of detecting anomalies in complex time series data. With the continuous optimization of forecasting models and methods, prediction-based anomaly detection techniques have shown significant development potential. [PERSON] et al. (2021) applied forecasting methods to detect anomalies in power data. [PERSON] et al. (2023) proposed using seasonal thresholds to improve prediction-based detection methods, addressing the challenge of determining error thresholds. Table 1 presents a comparison of various anomaly detection methods. With the further advancement of deep learning, predictive models now consider not only the temporal dependencies of samples but also the spatial dependencies, including the network topology of the samples. [PERSON] et al. (2024) extracted spatio-temporal features from samples to predict and identify driver activities. [PERSON] et al. (2025) proposed a one-dimensional hybrid CNN-LSTM method to extract spatio-temporal features for detecting pipeline leaks. [PERSON] et al. (2016) used CNN and LSTM for deepfake video detection. [PERSON] et al. (2017) introduced a data-augmented SSA-CNN-LSTM framework for fault prediction. [PERSON] et al. (2028) introduced a multi-feature extraction neural network model based on Convolutional LSTM for predicting PM2.5 levels in air quality forecasting, achieving effective integration of spatial correlations. [PERSON] et al. (2029) proposed a novel detection method based on CNN and LSTM networks to comprehensively explore spatio-temporal information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Applied Method** & **Advantages** & **Disadvantages** \\ \hline PCA and Improved & Achieves simple fault detection by & Limited by PCA’s linear nature, making it \\ PCA Methods & replicating and enhancing PCA’s data & challenging to handle complex nonlinear \\  & compression and reconstruction capabilities. & relationships; computationally intensive. \\ \hline \begin{tabular}{c} Refactoring-based \\ methods and \\ improved refactoring \\ methods \\ \end{tabular} & Provides a more effective accuracy & This approach offers a more effective means \\  & assessment method, improving the & of accuracy assessment, enhancing the \\  & precision of anomaly detection. & precision of anomaly detection. \\ \hline \begin{tabular}{c} Clustering Algorithms \\ and Improved \\ Clustering Algorithms \\ \end{tabular} & Scalable and efficient methods for & Sensitive to the choice of hyperparameters; \\  & anomaly detection. & requires a large amount of normal data for \\  & & training, potentially limiting the ability to \\  & & detect novel anomalies. \\ \hline \begin{tabular}{c} Prediction-Based \\ Anomaly Detection \\ Methods \\ \end{tabular} & \
\begin{tabular}{c} Capable of learning more complex \\ nonlinear temporal relationships and \\ representations of high-dimensional \\ time series. \\ \end{tabular} & Prediction errors can impact detection \\  & & performance; may lack adequate \\  & & error analysis. \\ \hline
\begin{tabular}{c} Seasonal Threshold + \\ Prediction Methods \\ \end{tabular} & Improves the determination of error & Assumptions about seasonality may not \\  & thresholds. & apply to all time series data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Various Anomaly Detection Methods. In the time domain, the focus is typically on the transient characteristics of a signal, which reflect how the signal changes over time and are suitable for monitoring dynamic variations. In contrast, in the frequency domain, complex time-domain signals are decomposed into components of different frequencies. Analyzing these frequencies effectively identifies and suppresses noise, with an emphasis on the periodic characteristics of the signal.