Time series classification is becoming more and more significant in many domains, yet current approaches frequently place computing economy below classification accuracy. In order to improve predictive performance while preserving computational efficiency, this study introduces Diverse Representative Time Series Features (DRTSF), a novel feature-based framework that integrates a wide range of transformations, including derivatives, Hilbert transform, discrete wavelet transform, fast Fourier transform, discrete cosine transform, and autocorrelation. DRTSF seeks to identify intricate patterns in time series data by merging several distinct representations into a single feature vector. Using all 142 datasets from the UCR collection, our analysis indicates that DRTSF outperforms top feature-based classifiers like FreshPRINCE. The results suggest that DRTSF could be especially useful for large-scale data analysis, providing a speed-accuracy trade-off that is similar to the cutting-edge Quant method. In contemporary data-driven applications, these findings underscore the possible benefits of combining several representations to handle the trade-off between computational cost and classification precision.