It is possible to bring about a smart forecasting measure through the internal AI capabilities of Power BI. One such capability of Power BI is the Forecast function in the line charts. This assigns forecast capabilities based on historical data while allowing an algorithm to make predictions about future values by capturing trends based on machine learning models built into Power BI.
Then, going further, we have the following steps to be washed down with practical, real-life examples:
Utilize Forecast Asset in Line Charts:
A line chart with the past data that you need to forecast should be created first.
Within the analytics pane of the visual, there is a forecast provision.
Enable the Forecast option and set it to the length that you require (e.g., 12 months, 1 year, etc.).
Of course, users can forecast future values based on historical data that is generated through built-in AI.
Live Update Based Dynamic on Filters:
Forecasts can dynamically adjust according to user-applied filters by using a dynamic range for date slicing or slicers. This can be achieved by:
Applying Slicers for dates or periods will allow users to filter the dataset and adjust the forecasting period.
The forecast will dynamically update according to the filter selections, maintaining the integrity of future predictions based on the filtered historical data.
Best Practices for Using Forecast in Power BI:
The main point is to have historical data in a fine enough granularity to generate useful forecasts (e.g., daily, weekly, or monthly data); the more fine-grained the data, the better the future trends can be predicted.
Adjust Forecast Periods: Forecast lengths should be adjusted according to the business context; for instance, anticipate sales volume for the entire quarter or up to a year.
Review the Accuracy of Forecasting: As usual, forecasts should regularly be re-analyzed as they are simply historical trends. Well, more accurate predictions in forecasting can also be achieved by using more data points or improving data quality.
Seasonality and Trends: Make sure Power BI knows the seasonal pattern by toggling the "Seasonality" setting in the Forecast options, assuming a proven seasonality in your data.
Custom Forecast Models: In case you want total control over the forecasting model, such as having custom machine learning models, you can hook in Azure Machine Learning or R/Python scripts inside Power BI so that you train your models based on your needs but use Power BI interactivity.
