Time Series Forecasting with Moving Averages in Python

Understanding historical trends is essential for making informed future predictions. Time series forecasting allows you to extract meaningful patterns from sequential data to guide strategic decision-making. In this text-based course, you will transition from a beginner to a confident practitioner capable of preparing sequential data, applying smoothing techniques, and constructing basic predictive models. You will learn how to identify trends and seasonal patterns using foundational moving average methods. What you'll learn: Understand the fundamental concepts of time series data, including stationarity, trends, and seasonality; Calculate simple, weighted, and exponential moving averages using modern Python data libraries; Apply smoothing techniques to reduce noise in sequential datasets and highlight underlying trends; Implement basic forecasting models using the statsmodels library and ARIMA frameworks; Evaluate forecast accuracy using standard error metrics like Mean Absolute Error (MAE); Write clean, type-hinted Python code to preprocess data and automate moving average calculations. The course begins with essential definitions and data preparation concepts before moving into step-by-step calculations. You will progress from basic moving average implementations to using statistical libraries for predictive modeling. This course is designed for data analysts, developers, and researchers new to time series analysis, with no prior forecasting experience required. Start analyzing your sequential data and building your first forecasting models today.