This thesis work proposes a novel approach to analyze time series anomaly detection. Time series consist of sequences of data points indexed in time order and play an important role in various industries, such as finance, healthcare, and energy production. The recent technological development has led to a huge amount of data in several fields, making human manual analysis impossible. Traditional statistical methods often assume that data come from specific mathematical models, struggling to scale to large datasets. In contrast, machine learning approaches treat data generation as a black box, relying on algorithms to identify patterns without explicitly modeling the generation process.