Are you interested in Power BI forecasting capabilities? Admittedly, forecasting is an essential part of data analytics. It empowers organizations to make informed decisions, predict future performance, and seize opportunities.
As a versatile business intelligence platform, Power BI offers several ways to perform forecasting using built-in features in Power BI Desktop and AutoML in Power BI service. We will also look into how to explore data for your forecasting projects, the good practices of Power BI forecasting and the limitations.
| Forecasting Type | Purpose | Algorithm | Power BI features |
| Time Series Forecasting | Predict continuous numerical trends over time. | Linear Regression | Line Chart Forecast Feature, DAX LINEST() Function, Regression in AutoML |
| Classification Forecasting | Predict categorical or binary outcomes (e.g., yes/no). | Logistic Regression | Binary Prediction in AutoML |
| Clustering | Group data points into distinct segments based on shared characteristics | K-Means | The clustering feature in scatterplots |
Power BI makes it possible to predict continuous numerical values, such as sales, revenue, or expenses, over a specified time period. This type of forecasting is commonly referred to as time series forecasting. Linear Regression in Power BI is commonly used for this type of forecasting.
Linear regression analyzes the relationship between a dependent variable (e.g., revenue) and independent variables (e.g., time). It fits a straight line to the data to predict future values based on historical trends.
Use Case
An e-commerce platform wants to forecast the revenue for the next financial year based on historical sales data. Using Power BI, you can:
By forecasting revenue trends, you can anticipate peak sales periods, allocate resources strategically, and plan marketing campaigns effectively.
In some cases, you may need to predict categorical outcomes, often represented as “yes” or “no.” This type of forecasting is known as classification. Power BI leverages Automated Machine Learning (AutoML) to simplify these tasks, allowing you to build predictive models efficiently. Behind the binary prediction AutoML function is a logistic regression algorithm.
Logistic regression estimates the probability of an outcome by analyzing the relationship between the dependent variable (binary or categorical) and independent variables. Data analysts can then decide to assign the forecasted value if it’s probability is more than 50/60/70/etc %.
Use Case
An SaaS subscription-based business wants to predict whether a customer is likely to churn. By analyzing customer data—such as usage patterns, complaints, and payment history—Power BI can:
This insight enables companies to proactively address potential customer churn by offering discounts or improving customer service.
Clustering involves grouping data into segments with shared characteristics. Power BI supports clustering through visualization tools like scatterplots and algorithms like K-Means clustering.
K-Means clustering partitions data points into a predefined number of clusters. It minimizes the distance between data points and their cluster centroids, grouping similar data together.
Use Case
An e-commerce platform wants to segment its customers into distinct groups based on their purchasing behavior (e.g., frequency of purchases, total spending, and product categories). Using Power BI, you can:
This segmentation allows businesses to create targeted marketing strategies to each cluster, enhancing customer engagement and boosting sales.
Any forecasting project begins with data exploration. Data exploration is the process of examining and understanding the dataset before performing forecasting. In Power BI, this step is particularly important as it ensures the accuracy, relevance, and reliability of the forecasts.
Let’s break down why data exploration is critical and how Power BI AI features, such as the Key Influencers Visual, can assist in this process.
The Importance of data exploration before forecasting
The Key Influencers Visual in Power BI is designed to analyze and display factors that significantly affect a particular outcome. This feature is especially useful during data exploration, as it identifies patterns and relationships that might not be immediately apparent.
Data exploration can also be done by building histograms and understanding the distribution within the data. Check our Power BI linear forecasting article to find a more detailed example of data exploration for forecasting purposes.
There are 3 methods to time series forecasting in Power BI: using the line chart forecasting feature, coding it in DAX and using AutoML. We will briefly discuss them below and give you links to more detailed guides.
Power BI’s Line Chart Forecasting Feature is a simple yet effective tool for generating quick forecasts. It requires minimal configuration and is perfect for analyzing trends over time.
Make sure you clean the dataset to remove any missing values or outliers that could affect the accuracy of the forecast.
Open your Power BI report and drag a Line Chart visual from the visualization pane onto the canvas. Add the Date/Time field to the X-axis and the Numerical field to the Y-axis. Configure the date field to show intervals such as months, weeks, or years.
Select the Line Chart visual. Open the Analytics Pane (available in the visualization options). Locate the Forecast option and click Add. Click Apply to generate the forecast.
The forecast will appear as an extended line on the chart, alongside the historical data. The confidence interval (shaded area around the forecast line) indicates the range of possible values based on statistical probability.
You can read more about configuring this type of forecast in our line chart forecasting guide.
DAX (Data Analysis Expressions) allows for more advanced and customized forecasting by creating regression models.
The specific DAX formula for linear regression is Linest(). This formula allows you to specify the predicted variable and the specific variables to predict it based on.
Linest() creates a new table which gives the incept and correlation coefficients of every dependent variable with the independent variable. These coefficients can be then used for predictions based on linear regression.
Some of the other columns that are created using Linest() help users to measure the accuracy of their predictions. These are columns like Coefficient Of Determination, Standard Error, F Statistic, etc.
If you want to learn more about how to use Linest(), you can read more about it in our Power BI linear regression guide.
AutoML in Power BI is an advanced forecasting tool available in the Premium workspace. It uses the same linear regression algorithm to predict continuous variables, removing the need to code it in DAX.
The output of the linear regression algorithm can then be extracted into Power BI Desktop and be used in the DAX formulas. If you want to learn more about using AutoML for time series forecasting, refer to our Power BI linear regression guide.
In Power BI, binary prediction in AutoML (Automated Machine Learning) is a key tool for classification forecasting, especially for scenarios involving two possible outcomes (e.g., “Yes” or “No”). This capability helps businesses make informed decisions by predicting the likelihood of specific events.
AutoML in Power BI simplifies machine learning by automating the creation, training, and evaluation of predictive models. For binary prediction, AutoML uses historical data to forecast Yes/No outcomes for scenarios such as customer churn, loan approval, or product returns.
First determine your categorical target variable (e.g., “Churn” with values “Yes” or “No”).Predictor variables (e.g., “Payment History,” “Engagement Level”).Ensure the dataset is cleaned and labeled correctly. (See Sample Dataset below)
| Customer Id | Churn | Payment History | Engagement Score | Subscription Tenure (Months) |
| C001 | Yes | Delayed | 60 | 12 |
| C002 | No | On Time | 90 | 18 |
| C003 | Yes | Delayed | 50 | 36 |
Binary prediction using AutoML is only supported for Power BI dataflows. You can set up a Gen1 or Gen2 dataflow in Power BI service.
Training the Model:
Select Binary Prediction as the model type and configure the target variable and predictors. AutoML will train the model, producing predictions and evaluation metrics (e.g., accuracy, precision).
You will be able to extract the output of your prediction into Power BI Desktop the same way as with the linear regression. You will then be able to visualise it and add it to your DAX formulas for analysis.
Power BI offers built-in clustering capabilities, making it easy for users to apply clustering models without requiring advanced technical expertise.
A common visualization to display clustering results is a scatterplot, where each data point is plotted based on selected variables, and clusters are represented by distinct colors or shapes.
Steps to apply clustering in a scatterplot chart are:
1. Prepare Your Dataset
Ensure your dataset includes the relevant variables to cluster. Clean and preprocess your data to remove duplicates, outliers, or missing values.
2. Create a Scatter Plot Visualization
Open your Power BI report. Drag the Scatter Chart visualization from the Visualizations pane onto the canvas.
3. Apply Clustering
Select the scatterplot visual. Open the Analytics Pane (available in the visualization options). Locate the clustering option and click add. Power BI will automatically detect natural clusters in the data based on the chosen variables.
4. Customize Clustering Parameters
Adjust the number of clusters if necessary. Power BI automatically chooses the optimal number of clusters, but you can manually set a specific value depending on your analysis needs. Rename clusters for easier interpretation.
5. Visualize and Analyze Clusters
The scatterplot will now display data points grouped into distinct clusters, represented by different colors or symbols. Hover over each data point to see additional details about its cluster assignment.
You can read more about clustering in our Power BI AI features article.
Below are some best practices to optimize your forecasting experience in Power BI:
Forecasting models depend heavily on high-quality data. Inconsistent or incomplete data can lead to unreliable predictions.
Remove duplicate records, missing values, and outliers. Ensure data fields are correctly formatted, especially date/time and numerical fields.
Forecasts are valuable only when they align with your organization’s goals and priorities.
Define the specific metric or variable you want to forecast (e.g., monthly revenue, customer churn).Incorporate domain-specific knowledge to interpret trends effectively.
Exploring your data before forecasting helps identify patterns, seasonality, and correlations.
Use visuals like line charts, scatterplots, or Power BI’s Key Influencers to analyze your dataset. Look for trends or cycles that should be included in the forecasting model.
Different scenarios call for different forecasting approaches, like time series, classification, or clustering.
Use the Line Chart Forecast Feature for quick time series predictions. Apply AutoML for advanced classification tasks like predicting churn. Use clustering techniques for segmentation analysis.
When you forecast using AutoML, Power BI automatically generates some reporting on the forecast accuracy for you. You can use this reporting to change the forecasting parameters and optimise the accuracy metrics over time.
Split your data into training and test sets to evaluate the model’s performance. Monitor key metrics like R-squared, Mean Absolute Error (MAE), and Precision-Recall.
Below are some limitations of forecasting experience in Power BI:
Power BI forecasting features primarily focus on numerical data (e.g., revenue, sales). This means that you need to transform the text fields into numerical in Power Query before you can use algorithms like linear and logistic regressions.
Predicting non-numerical data, such as textual trends or sentiment analysis, also often requires external tools.
Forecasting accuracy is heavily reliant on the quality and completeness of the dataset. Missing values and outliers may skew the results of your forecasting.
As with every forecasting project, the more data you have, the more accurate your forecasts are. For example it is difficult to predict next year performance based on only 2 years of data. However if you have 10 years of data, the predictions will be more accurate.
Built-in tools like the line chart “forecast” feature provide quick results but lack the flexibility for complex forecasting scenarios. More advanced modeling often requires Power BI Premium’s AutoML capabilities or integration with external tools.
While Power BI is designed to manage large datasets, performance issues may arise during forecasting with extremely high volumes of data.
Power BI offers foundational algorithms (e.g., linear regression, clustering), but users looking for cutting-edge methods like neural networks or support vector machines may need to use specialized machine learning platforms.
We have covered different forecasting techniques available in Power BI through this article, including time series forecasting, classification forecasting, clustering, and advanced methods like DAX and AutoML. Each technique is designed to cater to specific analytical needs, helping users predict trends, categorize outcomes, and group data effectively.
These tools not only simplify complex forecasting but also enable businesses to make data-driven decisions confidently. By applying these methods, users can unlock valuable insights, improve strategic planning, and stay ahead of industry challenges. Power BI truly empowers its users to transform data into actionable predictions for smarter decision-making.
If you want a professional Power BI consultancy to create predictions based on your data, reach out to us now!
