Time Series Analysis for Supply Chain Demand Forecasting

Anticipating customer demand accurately is the backbone of efficient supply chain management. This course introduces you to the core principles of time series analysis, enabling you to extract meaningful insights from historical data. You will progress from understanding foundational statistical properties to reading and applying modern forecasting models. By studying these written guides and clear code examples, you will gain the skills needed to identify patterns, address data anomalies, and build robust demand prediction pipelines. What you'll learn: - Understand core time series concepts such as stationarity, trends, seasonality, and cyclicality. - Analyze historical patterns using autocorrelation and partial autocorrelation techniques. - Explore classical forecasting models, including autoregressive and moving average approaches. - Apply modern data preparation workflows using Python libraries to clean and structure time series data. - Evaluate forecasting performance using modern error metrics to ensure model reliability. - Address real-world supply chain challenges like data drift and sudden demand shifts. The course begins with essential terminology and structural concepts before guiding you through data analysis techniques and practical forecasting models. You will read through step-by-step explanations and study clear code implementations designed to build your confidence. This course is designed for beginners in data analytics, supply chain planning, or operations who want to understand time series fundamentals. No prior forecasting experience is required. Start building your foundational skills in demand prediction today.