- Key Forecast Options in Power BI
When using the built-in forecasting feature, you can configure the following options:
a. Forecast Length
• Specify how far into the future you want to forecast (e.g., 12 months, 24 months).
• This determines the number of data points Power BI will predict.
b. Confidence Interval
• Set the upper and lower bounds for the forecast (e.g., 95% confidence interval).
• This provides a range within which the actual values are likely to fall.
c. Seasonality
• Specify the seasonality period (e.g., 12 for monthly data with yearly seasonality).
• Power BI uses this to account for recurring patterns in the data.
d. Ignore Last
• Exclude a certain number of data points from the forecast calculation.
• Useful if the most recent data is incomplete or unreliable.
2. Advanced Forecasting with DAX
For more advanced forecasting, you can use DAX (Data Analysis Expressions) to create custom
calculations. Examples include:
• Moving Averages: Calculate rolling averages to smooth out trends.
• Linear Regression: Use statistical functions like LINEST to predict future values.
• Exponential Smoothing: Apply weighted averages to emphasize recent data points.
3. Integration with Azure Machine Learning
Power BI can integrate with Azure Machine Learning to leverage advanced forecasting models,
such as:
• ARIMA (AutoRegressive Integrated Moving Average)
• Prophet (developed by Facebook for time series forecasting)
• Neural Networks
This requires setting up an Azure Machine Learning workspace and deploying a model.
4. Custom Visuals for Forecasting
Power BI supports custom visuals from the AppSource marketplace that can enhance
forecasting capabilities. Examples include:
• Forecasting Chart by MAQ Software: Provides advanced forecasting options.
• Time Series Forecasting by Microsoft Research: Offers robust time series analysis tools.
5. R and Python Integration
Power BI allows integration with R and Python scripts for advanced forecasting. You can:
• Use libraries like forecast in R or prophet in Python.
• Create custom visualizations and predictions directly in Power BI.
6. Quick Insights
Power BI’s Quick Insights feature can automatically detect trends and patterns in your data,
which can be used as a starting point for forecasting.
7. Scenario Analysis with What-If Parameters
While not strictly forecasting, What-If Parameters allow you to simulate different scenarios and
predict outcomes based on variable inputs.
Forecast Charts
1. Prepare Your Data: Ensure your data has a time-based column and is clean.
2. Create a Line Chart: Add a line chart to your report and include the time-based column
and the metric you want to forecast.
3. Enable Forecasting: Go to the Analytics pane, select the line chart, and enable the
Forecast option.
4. Customize Settings: Adjust the forecast length, confidence interval, and seasonality as
needed.
5. Analyze Results: Review the forecasted values and confidence intervals to make data
driven decisions.
References:
Describing the forecasting models in Power View | Microsoft Power BI Blog | Microsoft Power BI
