Time series data are pivotal in diverse fields such as finance, meteorology, and health data analysis. Accurate analysis of these data is crucial for identifying temporal trends and making informed decisions. However, frequent occurrences of missing values, often due to device failures or data collection errors, pose a significant challenge. In this work, we introduce TS-Pothole, a method for imputing missing values in univariate time series. This method leverages cyclic pattern analysis and a recursive strategy to handle univariate datasets in which missing values are distributed both continuously and randomly. We evaluate TS-Pothole on four real-world datasets representing different configurations of missing values, and assess its performance in terms of accuracy and execution speed. In particular, we compare our approach with stateof-the-art methods, such as GANs and autoencoders. Our experiments show that TS-Pothole outperforms such methods by providing more accurate (up to 1.5 times) and faster (up to 2 times) imputations, even as the proportion of missing data increases, representing the best alternative in handling univariate time series with missing values.