# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 1 Introduction  
The sequential and temporal nature of time-series data makes them an essential part of many fields, such as manufacturing, healthcare, finance, and environmental monitoring. To ensure system security, dependability, and efficiency, it is essential to be able to analyze and identify anomalies in time-series data. Data points that substantially deviate from expected patterns are called anomalies, sometimes known as outliers or deviants. These deviations frequently point to important events that could have serious operational or financial repercussions, such as sensor malfunctions, equipment failures, cyberattacks, fraudulent transactions, or other anomalous behaviors [1, 2]. Industrial environments, especially vital infrastructures such as manufacturing facilities, energy grids, and water management systems, are among the most important places for anomaly detection to be used. These systems are made up of several linkeddevices that frequently function in intricate and changing environments. To guarantee seamless operations, avoid system failures, and reduce the risks associated with natural or human-made disruptions, these infrastructures must be continuously monitored and managed. However, conventional threshold-based monitoring methods, which depend on static limits or pre-established rules, frequently cannot keep up with the complexity and unpredictability of actual industrial processes. Adoption of more sophisticated, data-driven anomaly detection techniques is required due to the unpredictability of operational environments, the existence of noise in sensor readings, and the changing behavior of interconnected components. The creation of a baseline that reflects typical operational behavior is a crucial first step in the detection of industrial anomalies. It is possible to identify deviations that might point to possible threats or failures by comprehending and simulating typical system behavior. Predictive maintenance techniques can be created by utilizing past trends and patterns to identify and fix anomalies before they become serious issues. This method increases the resilience of vital infrastructures, reduces downtime, and allocates resources optimally. However, the lack of labeled anomalous data is a fundamental problem in these systems. Industrial anomaly detection frequently suffers from a severe imbalance between normal and abnormal instances, in contrast to traditional machine learning tasks, where labeled datasets are plentiful. It is challenging to build supervised learning models that rely on substantial amounts of labeled training data because anomalies in these systems are uncommon, unpredictable, and highly context-dependent. Researchers have been concentrating more on unsupervised and semi-supervised techniques that do not require explicit labels for anomalies because of the shortcomings of supervised learning in industrial anomaly detection. Clustering [3] and density estimation [4] are two examples of unsupervised techniques that look for anomalies based on statistical characteristics or departures from learned distributions. In a similar vein, semi-supervised methods concentrate on simulating typical behavior and identifying departures from the learned models. Reconstruction and forecasting methods based on deep neural networks are a commonly used class of models for this purpose. Reconstruction-based models [5] identify anomalies based on their inability to be accurately reconstructed, while autoencoders or generative models are used to encode normal patterns. Conversely, forecasting-based approaches [6] identify deviations as possible anomalies and forecast future values based on past observations. The ability to detect anomalies in time-series data has been further improved by recent developments in deep learning and representation learning. Contrastive representation learning is one promising method that has recently been used for time-series anomaly detection [7] and has gained popularity in computer vision tasks [8; 9].