Time series data, unlike static machine learning (ML) data, requires specialized tools and libraries for processing, making it crucial for data scientists and ML engineers to select the right resources. The article provides an extensive overview of tools and packages beneficial for time series projects, categorizing them into data preparation and feature engineering, data analysis and visualization, experiment tracking, and forecasting. It highlights popular Python-based tools like Pandas and NumPy for data manipulation, Matplotlib and Plotly for visualization, and advanced libraries like Statsmodels, PyTorch, and TensorFlow for forecasting. The significance of experiment tracking tools such as neptune.ai and Weights & Biases is emphasized for managing time series models efficiently. The article concludes with a comparison of various forecasting libraries based on their features, release year, and popularity, offering a comprehensive guide for effectively handling time series data across various domains.