Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

The growing demand for an efficient approach to classify time series data is bringing forth numerous research efforts in data mining field. Popularly known applications like business, medical and meteorology and so on, typically involves majority of data type in the form of time series. Hence, it is crucial to identify and scope out the potential of time series data owing to its importance on understanding the past trend as well as predicting about what would occur in future. To efficiently analyze the time series data, a system design based on Sliding Window Technique-Improved Association Rule Mining (SWT-IARM) with Enhanced Support Vector Machine (ESVM) has been largely adopted in the recent past. However, it does not provide a high accuracy for larger size of the dataset along with huge number of attributes. To solve this problem the proposed system designed a Sequence Mining algorithm-based Support Vector Machine with Decision Tree algorithm (SM-SVM with DT) for efficient time series analysis. In this proposed work, the larger size of the dataset is considered along with huge number of attributes. The preprocessing is performed using Kalman filtering. The hybrid segmentation method is proposed by combining a clustering technique and Particle Swarm Optimization (PSO) algorithm. Based on the sequence mining algorithm, the rule discovery is performed to reduce the computational complexity prominently by extracting the most frequent and important rules. In order to provide better time series classification results, the Support Vector Machine with Decision Tree (SVM-DT) method is utilized. Finally, the Pattern matching-based modified Spearmen's rank correlation coefficient technique is introduced to provide more similarity and classification results for the given larger time series dataset accurately. The experimental results shows that the pro-

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