In recent years, multi-modal time series analysis has gained increasing attention for integrating insights from diverse data sources, enhancing both predictive performance and explanations across various domains. This growing interest is driven by the rapid production of heterogeneous data in the real world, where temporal signals often come not only in numerical form but are also accompanied by complementary modalities such as text, images, or structured metadata. By effectively leveraging information from different modalities, researchers can discover richer patterns and improve model performance in the application. We plan to present a comprehensive half-day tutorial at AAAI 2026, tailored for researchers and practitioners interested in multi-modal time series analysis. This tutorial provides insights into the theoretical and practical aspects of multi-modal time series, covering data characteristics and cross-modality modeling strategies for various downstream tasks. Attendees will also learn best practices for applying multi-modal time series analysis to real-world domains such as finance, healthcare, and transportation. Through these examples, participants will gain a clearer understanding of how to move from theoretical modeling to impactful deployment. The tutorial offers a comprehensive and in-depth understanding, practical skill development, and networking opportunities, connecting theory with real-world applications.