Counterfactual explanations are valuable tools in Explainable AI (XAI) for understanding complex machine learning models. While successful in tabular and image data, existing optimization-based counterfactual methods struggle with time series due to generated explanations containing unrealistic high-frequency oscillations, which significantly hinders their plausibility. This paper proposes a novel approach to address this issue. Our method leverages the Discrete Fourier Transform (DFT) to eliminate oscillations from counterfactual explanations generated by any optimization-based technique. This allows for the utilization of established counterfactual explanation algorithms from other domains while ensuring the resulting explanations are temporally coherent and interpretable for time series data. We demonstrate the effectiveness of our approach on time series classification tasks using datasets from the University of California Riverside (UCR) archive. Experiments show significant improvement in the plausibility of counterfactual explanations generated by three state-of-the-art methods. This paves the way for a more efficient application of existing counterfactual explanation techniques to the time series domain.