- The detection of asynchronous periodic patterns in multivariate time series is a critical task in various fields such as finance, healthcare, and environmental monitoring. Traditional periodic pattern detection methods often assume synchronicity across different time series, which may not hold in real-world applications. This paper surveys the state-ofthe-art frameworks for detecting asynchronous periodic patterns in multivariate time series. We categorize existing methods based on their underlying principles, such as signal processing techniques, machine learning models, and hybrid approaches. Additionally, we highlight the strengths and limitations of each framework and provide insights into potential future research directions.