What you'll learn

Business data often changes over time, yet standard machine learning workflows do not always account for temporal order, seasonality, or forecasting horizons. In this course, Introduction to Time Series Modeling, you’ll gain the ability to recognize time series problems and frame them for practical analysis and forecasting. First, you’ll explore what makes time series data different from traditional datasets and how forecasting, anomaly detection, and pattern analysis relate to business questions. Next, you’ll discover how trend, seasonality, cycles, irregular variation, and additive or multiplicative structure influence modeling decisions. Finally, you’ll learn how to decide when time series approaches are appropriate compared to simpler methods such as aggregation, regression, or rules. When you’re finished with this course, you’ll have the skills and knowledge of introductory time series modeling needed to frame forecasting problems and choose practical approaches for business data.