Uncertain data occurs in several scientific and measurement-intensive applications. It aids in a more realistic understanding of variability in the time series, which is evident in real-world applications. The uncertain data accounts for the uncertainty, error ranges, and variability of the data. The existing visualization techniques for uncertain time series have notable limitations, including color mixing, over plotting, and a tendency to present data as if it represents a fixed range of values, which obscures the underlying uncertainty. These challenges reduce interpretability and can mislead users about the data’s variability. To address these issues, this thesis proposes an approach to stippling, which enhances the readability and informativeness of such plots. Stippling uses dots similar to scatter plots. The dots differ by size and placement according to characteristic parameters, offering an intuitive means of conveying density in certain regions. The stippling method employs Voronoi cells for dot placement, which effectively prevents overlap and preserves clarity. An algorithm is developed to visualize multiple uncertain time series using stipples. The algorithm uses Voronoi centers to place the dots to prevent clutter and effectively express density through the number of dots. To assess the approach’s effectiveness, the algorithm is additionally evaluated in terms of visual quality and runtime performance, examining how well it supports accurate interpretation without compromising efficiency. Furthermore, a pilot study is conducted to gather insights into the accuracy and compare its benefits and limitations from a user perspective.