These methods first encode and compress the input into latent variables and then decode it back to a state close to the original dataset, ensuring the AE learns only the essential information in the data [15]. Since AE lacks specific targets, they adopt an unsupervised training approach to learn data representation [16]. However, the massive influx of high-dimensional time series data generated in Internet of Things (IoT) and smart manufacturing environments poses challenges that conventional AE cannot address due to the inherent time series properties. Inspired by [PERSON] [17], researchers have begun integrating Recurrent Neural Networks (RNNs) with AE, proposing a series of hybrid models. By leveraging RNNs' ability to remember past information, these models aim to handle multivariate time series data. To mitigate the gradient vanishing and exploding issues in RNNs, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used as substitutes. Experiments in skeleton-based gait anomaly recognition have demonstrated the capabilities of these models, with Long Short-Term Memory Autoencoder (LSTM-AE) combinations outperforming Gated Recurrent Unit Autoencoder (GRU-AE) [18]. LSTM-AE is widely used as a foundational approach for time series anomaly detection and has spawned numerous variants. [PERSON] et al. [19] proposed a Variational Autoencoder (VAE) based on LSTM, successfully detecting anomalies in robot-assisted feeding with superior performance to baseline models. [PERSON] et al. [16] applied Bi-directional Long Short-Term Memory (Bi-LSTM) networks to AE, where one unidirectional LSTM processes data from past to future and the other from future to past, enhancing anomaly detection accuracy in smart metering systems. [PERSON] and [PERSON] [20] demonstrated that Bi-LSTM-AE outperformed conventional LSTM-AE on wind power datasets. However, these studies all build on LSTM, inheriting its inherent properties. [PERSON] and [PERSON] [21] argued that due to the gate connectivity approach of conventional LSTMs, this can lead to loss of necessary information and a decline in network performance. [PERSON] et al. [22] proposed Working Memory Connections (WMCs) for LSTM, providing an efficient way to utilize intra-cell knowledge within the network. This design significantly outperformed vanilla LSTM and addressed key issues in previous formulations (LSTM with peephole connections). It has been validated in two different toy problems, as well as in tasks such as digit recognition, language modeling, and image captioning. However, there has been no example of its use in the field of anomaly detection. We believe this variant can enhance existing LSTM-based anomaly detection algorithms by allowing memory cell state to influence gates, thereby improving information flow control and making anomaly detection algorithms more sensitive. Recently, several studies have introduced time series anomaly detection methods that do not rely on LSTM. [PERSON] et al. [23] proposed a method named AnomalyBERT, a self-supervised Transformer-based approach for time series anomaly detection, which leverages a novel data degradation scheme with synthetic outliers to effectively capture temporal contexts and inter-variable relationships. [PERSON] et al. [24] proposed a method named DCdetector, a dual-attention contrastive representation learning algorithm for time series anomaly detection, which enhances representation differences between normal and anomalous points without relying on reconstruction error. [PERSON] et al. [25] proposed a calibrated one-class classification method for unsupervised time series anomaly detection (COUTA), leveraging Temporal Convolutional Networks and dual calibration techniques to achieve contamination-tolerant, anomaly-informed normality learning with state-of-the-art performance. However, it is worth noting that no universally optimal model or approach currently exists in the academic community.