Time Series Forecasting using DAX in Power BI

Time series data is a sequence of data points collected over time. It's useful for identifying trends, seasonal patterns, and random variations, making it essential for forecasting and decision-making in finance, economics, and other fields.

In this section, you'll learn how to construct the Trend Measure in Power BI using DAX. The Trend Measure helps identify and isolate the underlying direction of your data over time, separate from seasonal fluctuations. By calculating this measure, you'll capture long-term patterns that are crucial for making accurate time series forecasts. This step is essential for understanding the baseline trend before layering on seasonality or other factors.

This section focuses on creating the Seasonality Measure using DAX in Power BI. You'll learn how to capture recurring patterns in your data, such as weekly, monthly, or yearly fluctuations, by analyzing historical data trends. This measure helps adjust your forecast for predictable cycles, providing a more accurate prediction by accounting for regular variations over time. Seasonality is essential for refining your time series model and ensuring it reflects real-world cyclical behavior.

In this lesson, you'll combine the Trend and Seasonality measures to create a full Sales Forecast. You'll also implement the Actualized Forecasted Revenue, which replaces forecasts with actuals when available, ensuring dynamic and accurate forecasting in Power BI. This final step brings all components together for a complete time series model.

In this section, you’ll learn how to effectively visualize your time series forecasts using Power BI. We'll cover the creation of line charts, trend lines, and comparison visuals that clearly display your forecasts alongside actuals, allowing for easy interpretation of trends, seasonality, and forecast accuracy.

In this final section, we’ll review the key concepts and techniques covered throughout the course. You’ll recap how to build trend and seasonality measures, combine them for accurate forecasts, and visualize results effectively in Power BI. By the end, you’ll have the tools and knowledge to confidently apply time series forecasting in real-world scenarios, ensuring more informed, data-driven decisions.