Imagine teaching AI a new language, specifically designed for predicting the future. That's the core idea behind VITRO, a groundbreaking approach to time series forecasting. Traditional AI models struggle to grasp the nuances of time-dependent data like stock prices, weather patterns, or energy consumption. Their vocabularies, built for understanding human language, aren't suited for the continuous, numerical nature of time series. VITRO tackles this problem by creating a specialized vocabulary of 'pseudo-words,' each representing a unique time series pattern. These pseudo-words act as a bridge, translating complex temporal dynamics into a language AI can understand. Like giving a chef a new set of specialized knives, VITRO equips AI with the tools to dissect and interpret time series data more effectively. This innovative method, inspired by a technique called 'textual inversion' used in image generation, learns these pseudo-words by optimizing their representation within the AI model's existing vocabulary space. The result? Improved long-term forecasting accuracy across various datasets, from weather predictions to electricity demand. VITRO's potential extends beyond just better predictions. Its qualitative analysis reveals distinct patterns in how the AI model attends to and processes time series information, suggesting a deeper, more specialized understanding of temporal dynamics. This breakthrough opens doors to more accurate and insightful predictions in countless fields, paving the way for AI-driven decision-making in a world increasingly reliant on understanding the flow of time.