Mislabeled data in large datasets can quickly degrade the performance of machine learning models. There is a substantial base of work on how to identify and correct instances in data with incorrect annotations. However, time series data pose unique challenges that often are not accounted for in label noise detecting platforms. This paper reviews the body of literature concerning label noise and methods of dealing with it, with a focus on applicability to time series data. Time series data visualization and feature extraction techniques used in the denoising process are also discussed.