This tutorial provides a step-by-step guide to time series forecasting. The first part is aimed at beginners, demystifies the core components of time series data, such as trend, seasonality, and noise, and guides through the essential steps of preparing data for predictive modeling. The second part analyses the most common forecasting techniques, from classical statistical methods to modern machine learning to deep learning approaches. Each method is illustrated with examples to help understand how and when they can be used effectively.