# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 2 Literature Review
Furthermore, the optimization of BFAE relies on traditional gradient descent techniques combined with Frechet derivatives, allowing for computations of functional gradients that are essential for training the model. In contrast to conventional sequential models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) that operate with fixed parameters, the BFAE provides a more nuanced framework that accommodates feature effects that dynamically change over time intervals. Overall, this advancement in functional data analysis presents promising applications across various domains, enabling improved efficiency and accuracy in the analysis of temporal data. The article by [PERSON] [20] presents LMS-AutoTSF, which distinguishes itself from traditional forecasting models by employing dual encoders that operate at multiple scales and is specifically designed to handle the nuanced variations in time-series data. These encoders utilize learnable filters to separate trend and seasonal components dynamically, employing both low-pass filters to capture long-term trends and high-pass filters for seasonal variations. This effectively allows the model to isolate and analyze the intricacies of the data in the frequency domain, a feature that is particularly beneficial for multivariate time-series forecasting, where different variables may interact and influence their temporal behaviors. The architecture integrates autocorrelation to enhance temporal modeling by computing lagged differences, thus capturing dependencies across time steps more efficiently than prior methodologies. Furthermore, the article elaborates on the challenges inherent in time-series forecasting, including the presence of linear and nonlinear trends, seasonal fluctuations, and the dynamic nature of the datasets, including those used for traffic forecasting (PEMS datasets) mentioned in the analysis. Traditional algorithms often struggle with these complexities; however, the LMS-AutoTSF framework combines frequency-domain filtering with temporal and channel-wise transformations, granting it an edge in discerning and processing detailed temporal patterns across multiple features and data dimensions. Consequently, this makes the model not only more effective in capturing long-term dependencies but also more efficient in processing, ensuring high precision while maintaining a lightweight design conducive to faster prediction times. The authors of [21] introduce an approach within the context of forecasting. Transformers have demonstrated significant capability in sequence modeling; however, they face scalability issues and performance degradation when dealing with multivariate series that include large lookback windows. The authors contend that standard Transformer architectures tend to merge multiple variables into single temporal tokens, impairing the model's ability to learn meaningful representations and resulting in ineffective attention maps that fail to capture nuanced correlations. The proposed iTransformer architecture adopts an inverted approach, meaning that it separates the temporal and variable dimensions to enhance representation learning. The normalization process transforms individual variable representations into a Gaussian distribution, helping to mitigate inconsistencies caused by measurement discrepancies. This adjustment improves the model's ability to effectively tackle non-stationary problems commonly found in time-series data. The authors utilize feed-forward networks (FFNs) customized for individual variable tokens, leveraging the universal approximation theorem to uncover complex relationships within the data. By doing so, the architecture efficiently encodes observed time-series data and decodes them for future predictions, demonstrating superior performance across seven real-world datasets compared with existing models such as Autoformer and LSTNet. The authors of [22] introduce TimeMixer, a novel model designed for time-series forecasting that fundamentally utilizes a multiscale mixing approach.