Top Shift Left security Best Practices CTOs should consider

Remember our last Power BI forecasting guide? It uncovered what really blocks accuracy.

Now it's time to take the next step. Knowing Power BI challenges is just the start - mastering forecasting techniques is what truly moves the needle.

In this guide, we will explore how to do forecasting in Power BI accurately.

Plus, we will look at the setup steps, the forecasting models that Power BI utilizes, and the measures that enable you to transform Power BI into a future-predicting asset.

But worry not! This guide will help you overcome this concern as we will be walking through the ins and outs of ELT vs ETL, their use cases, when to use what and much more.

Top Shift Left security Best Practices CTOs should consider Remember our last Power BI forecasting guide? It uncovered what really blocks accuracy. Now it's time to take the next step. Knowing Power BI challenges is just the start - mastering forecasting techniques is what truly moves the needle. In this guide, we will explore how to do forecasting in Power BI accurately. Plus, we will look at the setup steps, the forecasting models that Power BI utilizes, and the measures that enable you to transform Power BI into a future-predicting asset. But worry not! This guide will help you overcome this concern as we will be walking through the ins and outs of ELT vs ETL, their use cases, when to use what and much more. Understanding the Forecasting models in Power BI Power BI doesn't simply guess; it actually uses the ETS (Error, Trend, Seasonality) algorithm for forecasting accuracy. Now, ETS are of two types here: - ETS AAA model (also called as “Holt-Winters algorithm”). - ETS AAN model (also known as “Holt's linear model”). Both Power BI and Microsoft Excel use these BI forecasting models for Predictive analytics. Depending on the pattern of your historical data, they will automatically pick the best one. Which model does Power BI use for forecasting? VS Vinod Satapara CTO & Director iFour Technolab 15+ Years in Security & DevOps It actually depends on your past data. If your data is non-seasonal, then ETS AAN (Additive Error, Additive Trend, No Seasonality) model is applied. And for a seasonal pattern, the ETS AAA (Additive Error, Additive Trend, Additive Seasonality) model is applied. If your forecast looks like a flat line instead of waves, Power BI probably didn't detect seasonality. That means it's using ETS AAN. In that case, you'll need to enforce seasonality manually. How to do Forecasting in Power BI? (step-by-step) An important thing to note is that Power BI's built-in forecasting works only on line charts with time-series data. Other Power BI templates, like combo or area charts, don't have this support. To forecast in Power BI, first, you need to turn on the “Forecast” option. Here is how you can do it: Selecting line chart (with time-series data) > Analytics Pane > Forecast. Once done, you can proceed customizing - confidence intervals, forecast length, seasonality, and apply all these changes. Now you'll see predictions for future periods. Detailed step-by-step guide: Your data strategy deserves the best. Choose our Azure Synapse Analytics Consulting services . Inquire Now 1. Add a line chart On your report page, insert a line chart visual. 2. Add time-series data Drag a date or time field to the X-axis and a numeric value to the Y-axis. 3. Open the Analytics pane Select the line chart, then choose Analytics (magnifying glass icon) in the Visualizations pane. 4. Add a forecast Under Forecast, select Add. 5. Customize the forecast Expand Forecast options to set: Forecast length: How far ahead to predict (for example, 10 points or 12 months). (Tip: Keep this reasonable; long-term forecasts using ETS degrade in accuracy.) Confidence interval: Show a wider or narrower range (for example, 95%). Seasonality: This is a crucial step. If you know your data cycle (e.g., 12 months), enter 12 points manually rather than relying on "Auto." This forces the ETS AAA algorithm. Ignore last period: Exclude the last historical period if needed. 6. Apply changes Select Apply to update the chart. Tip: Use a continuous Date axis and clean, consistent granularity (daily, weekly, monthly) for better forecast behavior. Read More: 16 Power BI Dashboard Design Mistakes to Avoid Check this to learn how to use the Analytics pane in Power BI Desktop How To Measure Power BI Forecasting Accuracy Forecasting in Power BI is simple, but measuring “how close those predictions are to reality” is where real value - and trust - comes from. Here are the recommended tips to measure Power BI forecasting: Create a DAX measure to calculate forecasting accuracy. Choose a metric such as Mean Absolute Percentage Error (MAPE) or a simple % difference. Apply the measure to compare forecasted values against actual historical data. Use visuals like tables or charts to display accuracy results. Gain insights into how reliable your forecasts are over time. Need dashboards that drive decisions? Get started with our Dashboard Development Services . Contact Now Metrics Used To Forecast Power BI Accuracy Visualizing a line is easy; proving its accuracy requires statistics. Your Power BI dashboard should tell you exactly what's likely to happen next before your competitors even sense a shift. That's where you get the leap. To achieve this, you need to master Power BI forecasting techniques. Given below are some of the best techniques to measure forecasting accuracy in Power BI. 1. Mean Absolute Percentage Error (MAPE) Best for: Communicating accuracy to stakeholders in plain percentage terms. Definition: Measures the average percentage deviation between the forecast and the actuals. Interpretation: A lower MAPE indicates better performance. To learn more about its technical usage, check this out. 2. Mean Absolute Error (MAE) Best for: Understanding the average magnitude of errors in absolute units (e.g., dollars or units). Definition: The average absolute difference between predicted and actual values. Why use MAE: Unlike RMSE, it does not punish large errors excessively, giving a "linear" view of accuracy. Read More: 17 Power BI Myths and Misconceptions Busted | Uncover the Truth To learn how to use MAE technically with DAX formulas, visit this. 3. Root Mean Squared Error (RMSE) Best for: Identifying volatility and penalizing large outliers. Definition: The square root of the average of squared errors. Why use RMSE? If a single bad forecast could be terrible for your business (e.g., stockout), RMSE is the metric to watch because it magnifies large errors. You can learn more about calculating RMSE in Power BI here. 4. Rolling Forecast Comparison What it does: Compares forecasts made in previous cycles with actuals from the current cycle. Why Rolling Forecast is useful: Helps track accuracy over time and adjust models accordingly. How to use Rolling Forecast: Store historical forecasts and actuals in a matrix or table, then calculate error metrics month-over-month. Tip: Use matrix visuals with time slicers and error metrics. Empower leadership with clarity. Explore our CEO Dashboard Development Services now. Inquire Now 5. Visual Accuracy Checks What it does: Overlay forecast and actual lines in Power BI visuals. Why it's useful: Quick visual cue to spot divergence or alignment. How to use Visual Accuracy Checks: Use line charts with dual series — one for actuals, one for forecasts. Tip: Add confidence intervals or shaded bands to show prediction range. 6. Confidence Interval Analysis What it does: Evaluates whether actuals fall within the forecast's confidence band. Why it's useful: Adds context to forecast reliability. How to use: Use Power BI's built-in forecasting visuals with confidence shading enabled. Tip: Use shaded areas to communicate uncertainty clearly. Read More: 4 Types of Power BI Dashboards: Analytical, Strategic, Operational, and Tactical 7. Custom Python/R Scripts Purpose: Advanced statistical validation. Usage: Integrate external models for deeper diagnostics. Tip: Use Power BI's Python/R visual to run custom accuracy checks. Pro Tip: Combine multiple metrics to get a balanced view - MAPE for interpretability, RMSE for volatility, and visual overlays for stakeholder clarity. Future-proof your data strategy with a leading Microsoft Fabric Consulting Company . Begin Now How to do "What-If" Analysis in Power BI? Native forecasting is static. To model scenarios (e.g., "What if we grow 10% next year?"), use What-If Parameters. 1. Create Parameter: Go to Modeling > New Parameter > Numeric Range. Name it "Growth Rate" (e.g., 0 to 1 with 0.1 increments). 2. Create Measure: Build a DAX measure that multiplies your previous year's sales by this parameter. 3. Visualize: Add this measure to your chart. Users can now use a slider to see how different growth rates impact the trajectory. Learn “what-if” analysis in Power BI in detail here. Read More: 10 Executive Dashboard Examples for Consultants and CEOs Power BI Native Forecasting vs. Custom R/Python Visuals While Power BI's native tool is fast, it has limitations. Use this comparison to decide when to upgrade to R or Python scripts. Feature Native Power BI Forecast R / Python Custom Visuals Algorithm ETS (Exponential Smoothing) ARIMA, Prophet, Neural Networks, etc. Setup Speed Instant (Drag & Drop) Slower (Requires coding) Flexibility Limited (Seasonality, Horizon) Infinite (Full statistical control) Performance High (Integrated engine) Lower (Depends on local R/Python environment) Pro Tip: For most business dashboards, native forecasting is sufficient. If you require ARIMA modeling or external regressors (e.g., forecasting sales based on weather), you must switch to Python or R visuals. So, that's how decision-makers anticipate the trends effectively. I hope you found this blog informative and helpful. Want to forecast your business data accurately? Our Power BI consulting services help you do it confidently. Let's connect and discuss your needs. Power BI Forecasting Techniques: Conclusion In this blog, we learnt how to do Forecasting in Power BI. We also looked at the setup steps, Power BI forecasting models, and the measures that enable you to turn Power BI into a future-predicting asset. Forecasting accuracy in Power BI isn't just about numbers - it's about trust. By blending solid metrics like MAPE, MAE, and RMSE with clear visuals and confidence intervals, you create forecasts that stakeholders can rely on. Add advanced validation with Python or R when needed, and you've got a powerful balance of precision and clarity. That's how you turn Power BI into a dependable tool for smarter planning.

Lochan Paliwal

Lochan Paliwal, a skilled Content Writer at iFour Technolab, excels in SEO and creates impactful and engaging content. She loves exploring the latest technology trends and shares valuable insights on various platforms. She believes in learning and growing to make a meaningful impact in the digital field. Beyond writing, Lochan enjoys exploring new cuisines and travelling.