Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to deal with this issue. In this study, we propose folding the time series so that univariate time series can be turned into tabular data and using Multiple Imputation with Random Forest (MICE-RF) for imputation. Next, we compare this imputation strategy with state-of-the-art deep learning approaches (SAITS, BRITS, Transformer) for noisy, missing time series data in terms of MAE, F1-score, AUC, and MCC across missing data rates (10%–80%). Our results show that the proposed imputation strategy with MICE-RF can effectively impute missing data compared to deep learning methods. Importantly, our experiments also illustrate that, using an imputation algorithm on time series with missing data can, at the same time, offer denoising effects.