# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 2 Literature Review
TimeMixer incorporates two primary architectural components: Past-Decomposable-Mixing (PDM) blocks and Future-Multipredictor-Mixing (FMM) blocks. The PDM blocks facilitate the decomposition of complex time series into distinct components such as seasonal and trend elements, allowing for fine-to-coarse and coarse-to-fine information mixing and effectively capturing both microscopic and macroscopic variations within the series. The FMM blocks, conversely, leverage multiple predictor models to enhance forecasting accuracy by exploiting complementary insights derived from varied temporal patterns across multiple scales. This method addresses the inherent challenges posed by the non-stationary nature of real-world data, which often exhibits intricate variations due to various influences, including trends and seasonal fluctuations common in applications ranging from economics to traffic planning. The empirical results demonstrate that TimeMixer achieves state-of-the-art performance across multiple benchmark datasets, thereby validating the efficacy of its proposed multiscale strategy in forecasting tasks. The paper by [PERSON] [23] investigates the efficacy of Transformer models for long-term forecasting within time-series data, particularly emphasizing advancements made possible through self-supervised learning techniques. The author gives details about the architecture of the proposed model, PatchTST, which leverages the capabilities of Transformers. Transformers are highlighted as suitable candidates for modeling sequential data due to their effective attention mechanisms, which facilitate learning relationships across broader contexts in data, unlike previous models. Additionally, the paper evaluates existing models, including state-of-the-art frameworks such as Informer, Autoformer, and FEDformer, establishing them as baselines to benchmark performance improvements achieved through the PatchTST architecture. The authors of [5] present a novel approach to unsupervised anomaly detection in time-series data by leveraging a Transformer-based architecture, which is referred to as the Anomaly Transformer. The central methodology involves adapting Transformer models that utilize self-attention mechanisms to capture intricate temporal dynamics and relationships within the time series. Key innovations include the introduction of an association-based criterion for anomaly identification, which is co-designed with temporal models to enhance the learning of informative associations across time points, thereby addressing limitations found in prior methodologies that primarily focused on pointwise or pairwise representations. The Anomaly Transformer demonstrates superior performance when benchmarked against various existing anomaly detection methods, including local outlier factors and clustering techniques, underscoring the importance of temporal information in accurately identifying anomalies in complex datasets. The comprehensive evaluation across multiple datasets further emphasizes the model's robustness and generalizability, revealing a consistent state-of-the-art performance across benchmarks. OmniAnomaly [17] integrates VAEs with a stochastic RNN (Recurrent Neural Network) framework using GRUs to model temporal dependencies, while InterFusion [24] employs a hierarchical VAE to capture inter-metric and temporal relationships. These developments highlight the importance of being able to identify both temporal and cross-metric relationships that can significantly increase the accuracy of anomaly detection in complex datasets. GAN-based methods, such as MAD-GAN [18] (Multivariate Anomaly Detection for time-series data with a Generative Adversarial Network), use LSTM networks in both the generator and discriminator to detect anomalies. The GANs training framework allows for the creation of realistic data distributions that can be used to effectively identify deviations.