Time series clustering and classification remains a fundamental problem in many scientific and industrial domains, where the goal is to classify temporal patterns effectively and robustly. In this work, we introduce a novel approach, OPW-FA, an optimal transport based distance measure, which integrates the global alignment capabilities of Order Preserving Wasserstein distance with binned Fourier approximation of subsequences to capture both global and local temporal characteristics of time series data. We further propose an ensemble variant, EnOPW-FA, which aggregates multiple OPW-FA distances with diverse parameter settings to an ensemble classifier via majority voting, enhancing robustness across datasets with different characteristics. Through experiments on a subset of 30 datasets from the UCR Time Series Classification Archive, we demonstrate that OPW-FA achieves competitive accuracy compared to state-of-the-art methods such as ShapeDTW, BOSS, and standard OPW, while the ensemble variant further improves performance. A sensitivity analysis of OPW-FA’s parameters, such as bin sizes, window size ratios, step size ratios, and the number of Fourier coefficients, provides practical guidance for parameter tuning.