Modern Time Series Analysis and Forecasting

Time-dependent data is everywhere, from financial markets to weather patterns, yet analyzing it requires a specialized set of concepts. This text-based course guides you from the absolute basics of sequential data to building robust forecasting models. You will learn to recognize key patterns, prepare messy datasets, and apply both traditional statistical models and modern machine learning techniques to solve real-world prediction problems. What you will learn: 1. Understand foundational concepts including trends, seasonality, and stationarity. 2. Prepare and clean time-stamped datasets using modern data libraries like pandas and Polars. 3. Apply classical statistical forecasting models to capture linear relationships. 4. Implement machine learning approaches to model complex sequential patterns. 5. Explore modern deep learning concepts for sequence modeling, including LSTMs and transformers. 6. Evaluate model performance using appropriate validation metrics. The course begins with essential definitions and data preparation techniques, establishing a solid theoretical foundation. From there, you will progress through structured written explanations and code examples to implement statistical and machine learning concepts. This course is designed for beginner data analysts, programmers, and aspiring data scientists who want to work with sequential data. No prior experience with time series modeling is required, though a basic familiarity with Python is helpful. Start reading today to unlock the predictive power of your time-stamped data.