Adaptive Machine Learning Approaches for Time Series Pattern Detection: A Comprehensive Study

Abstract

Time series data is prevalent across several domains, encompassing fields such as finance and healthcare. The capacity to identify complex patterns within this data has the potential to reveal valuable insights and enhance prediction capabilities. The present paper offers a complete overview of adaptive machine learning techniques specifically designed for the purpose of detecting patterns in time series data. In this study, we assess a range of algorithms, encompassing conventional time series analysis methods as well as modern deep learning models. Our primary emphasis is on examining their capacity to adapt to evolving data dynamics. The experimental findings indicate that some adaptive algorithms exhibit exceptional resilience and precision when applied to a diverse range of synthetic and real-world datasets. In addition, we present a unique adaptive mechanism that utilizes principles of transfer learning, demonstrating its effectiveness in situations where the pattern structure undergoes temporal evolution. The present study offers a comprehensive comparative analysis that is valuable for both scholars and practitioners. Additionally, it sets the stage for future investigations into the integration of adaptive principles and machine learning techniques, aiming to improve the effectiveness of time series analysis.