Time Series Analysis: Forecasting Patterns, Trends, and Signals

This Time Series Analysis course teaches you how to turn sequential data into reliable insight and forecasts using practical Data Science methods. You will learn how to prepare, visualize, model, and evaluate time-based data so you can make stronger decisions in business, finance, operations, and beyond.

Master Time Series Analysis For Smarter Forecasting

• Build a solid foundation in Time Series Analysis, from time indexing and resampling to identifying trends, seasonality, and noise.
• Learn a practical, applied course with Professor Mark Davis on turning sequential data into reliable insight and forecasts for real-world decision-making.
• Develop confidence with core forecasting methods, including smoothing, exponential smoothing, Holt-Winters, AR, MA, and ARIMA models.
• Apply Data Science techniques to diagnose model fit, measure forecast accuracy, and communicate results clearly in a complete workflow.

Learn how to analyze time-based data, build forecasting models, and translate patterns into actionable predictions.

This course begins with the foundations of time series data and shows you how to work with time indexes, frequency, and resampling so your data is ready for analysis. You will then explore how to visualize underlying patterns, detect seasonality, and separate meaningful signals from noise. Along the way, you will strengthen your understanding of autocorrelation, lag relationships, and stationarity, which are essential for creating dependable models in Data Science.

As you move deeper into the course, you will learn decomposition methods for pattern discovery and compare baseline forecasting approaches with more advanced models. You will practice smoothing methods, exponential smoothing, and Holt-Winters forecasting before building intuition for classical Time Series Analysis techniques such as AR, MA, and ARIMA. The course also guides you through model identification, parameter selection, residual diagnostics, and forecast accuracy metrics, helping you choose and validate models with greater confidence.

You will also work through practical challenges such as missing values, outliers, multivariate time series, and explanatory variables, all of which appear in real Data Science projects. The final sections connect these skills to business, finance, and operations use cases, and the capstone integration shows you how to communicate findings and build a complete time series workflow. By the end of the course, you will be able to approach sequential data with a structured method, generate stronger forecasts, and present insights that support better decisions.