A Framework for Accurate Time Series Classification Based on Partial Observation

Time series classification problems are solved using a variety of algorithms. The success of each technique is earmarked by measures of efficiency and accuracy. In order to achieve efficiency and accuracy, existing methods detect the relevant data in segments within a time series. Within this trend, we propose a framework for Detecting Optimal Partial Observation (DOPO) in time series classification. The framework developed is applicable to any...