Time Series Analysis and Forecasting Model in Power BI

Learn how to use Power BI for time series exponential smoothing and handle errors using advanced Power Query techniques

In this course, students will learn about the forecasting models available in Power BI. By understanding how time series exponential smoothing works, students will be able to manipulate the forecast line efficiently for daily, monthly and yearly predictions of univariate data.

What you’ll learn

  • Visualise time series data in Power BI.
  • Apply and manipulate time series exponential smoothing forecast.
  • Transform unstructured data into time series data.
  • Understand time series theory, and the concepts of seasonal and cyclical data.
  • Handle time series forecasting errors using advanced techniques in Power Query.
  • Compare actual values versus forecast values.

Course Content

  • Introduction to Time Series Exponential Smoothing –> 5 lectures • 9min.
  • Time Series Forecasting and Advanced Error Handling –> 9 lectures • 1hr.

Time Series Analysis and Forecasting Model in Power BI

Requirements

  • A prior, basic understanding and usage of Power BI is recommended.
  • Prior experience using Power Query or writing M scripts is useful but not required.
  • An interest in stock trading is appreciated but not required.
  • Have Power BI desktop and Microsoft Excel installed.

In this course, students will learn about the forecasting models available in Power BI. By understanding how time series exponential smoothing works, students will be able to manipulate the forecast line efficiently for daily, monthly and yearly predictions of univariate data.

 

As part of the course, students will gain hands-on experience in advanced error handling techniques in Power Query and be able to tune parameters efficiently for cyclical and seasonal datasets.