The increasing volume and complexity of time series data require advanced methods for efficient data preprocessing, a critical step in deriving meaningful insights. This thesis, conducted in collaboration with AWAKE Mobility, explores the application of parallel processing frameworks to enhance the performance of preprocessing tasks. Specifically, it evaluates four frameworks: mpi4py, Charm4py, Dask, and dispy. The research focuses on their scalability, efficiency, and suitability for handling large-scale time series datasets. By implementing and benchmarking these frameworks across various preprocessing operations, the study provides a comprehensive analysis of their performance characteristics. The results demonstrate significant improvements in processing times and resource utilization, highlighting the potential of parallel computing to address the challenges associated with big data in time series analysis. This work contributes to the field by offering a comparative study that guides the selection of appropriate parallel processing tools for large-scale time series data preprocessing, ultimately facilitating more efficient data analysis workflows.