PREDICTIVE ANALYSIS OF HYBRID CLUSTERING METHODS FOR TIME SERIES DATA ANALYSIS

Abstract

Time series data analysis is essential in several fields such as finance, healthcare, climate research, and others. Conventional clustering methods sometimes encounter difficulties in efficiently processing time series data because of its sequential and temporal characteristics. Hybrid clustering methods have recently become popular for combining the advantages of several clustering algorithms to tackle issues effectively. This research thoroughly examines how hybrid clustering approaches may anticipate outcomes in time series data analysis. The research starts with a review of current literature on time series clustering algorithms, highlighting the drawbacks of conventional methods and the possible benefits of hybrid approaches. Various hybrid clustering strategies are analyzed, using a mix of partition-based, hierarchical, density-based, and grid-based clustering algorithms. Predictive analysis is used to evaluate the hybrid clustering models' capability to anticipate future trends and patterns in time series data. Autoregressive models, neural networks, and support vector machines are used with hybrid clustering approaches to improve prediction accuracy in forecasting. The experimental results show that some hybrid clustering strategies outperform standard clustering methods in terms of clustering quality and prediction performance. Hybrid methods that combine partition-based and density-based clustering algorithms provide favorable outcomes on many datasets. The paper also highlights important parameters that affect the effectiveness of hybrid clustering models, including distance metrics, feature selection approaches, and clustering ensemble tactics. This study gives useful insights on using hybrid clustering algorithms for analyzing time series data and provides practical recommendations for scholars and practitioners interested in applying clustering approaches for predictive analytics in time-varying datasets. The results enhance the current knowledge in time series clustering and open up possibilities for future study in this dynamic subject.