How to Use Power BI and Fabric for End-to-End Time Series Forecasting

Combining Power BI and Fabric gives you strong tools for time series forecasting. You can get data, handle it well, and make forecasts without needing outside help.

• Power Automate lets you link different platforms and gather all your data.

• Dataverse keeps your data neat and simple to use.

• AI tools in Azure and Microsoft 365 Copilot make hard forecasting tasks easier for you.

• With these tools, your team can work alone and get better at planning for the future.

You can use Microsoft’s built-in tools to make time series forecasting more correct and easier to use.

Key Takeaways

• Power BI and Fabric work together to help with time series forecasting. They help you from collecting data to making predictions.

• Get your environment ready by making workspaces. Install the tools you need. Connect your data sources with care.

• Clean your data well and look at it closely. This helps your forecast be more correct. It also helps people trust your results.

• Pick the best forecasting model, like Prophet. Train it by splitting your data the right way. Use good methods to check your model.

• Use Power BI to make charts and graphs of your forecast. Share your results with your team and others. Work together easily.

Setup

Prerequisites

Before you begin, check if your computer and accounts are ready. Use this table to see what you need:

You need the right subscriptions and access:

• A Power BI Pro license lets you make and share reports.

• Power BI Free users can only look at reports, not share them.

• For Fabric, use at least an F64 SKU for all features. Smaller SKUs like F2 or F8 are okay for small tests but cannot share.

• Power BI Premium users get Fabric services for free.

You should know some basic things before starting:

• Know how to make Power BI reports and use Fabric’s data lakehouse.

• Understand your data sources and how to connect them.

• Be able to use Fabric notebooks for forecasting, like with the Prophet library.

• Know how to set up data pipelines and refresh your data automatically.

• Learn about real-time analytics if you want to watch live data.

Tip: Decide what metrics you want to forecast. This helps you set up your reports and models the right way.

Environment Configuration

Get your workspace ready for forecasting:

1. Make a workspace in Power BI and Fabric. This keeps your work organized.

2. Install needed libraries in Fabric notebooks, like prophet for time series forecasting.

3. Turn on notebook environments in Fabric so you can run Python code and build models.

4. Connect your data sources. Use Power BI connectors to bring in data from files, databases, or cloud services.

5. Set up data refresh schedules to keep your data current.

You might have some setup problems:

• Outliers or missing data can hurt your forecasts. Clean your data first.

• Big datasets can slow down your work. Use aggregations and make your data model better.

• If you have trouble joining data from different places, use Power BI’s tools to combine and clean your data.

If you follow these steps, you will have a strong start for your time series forecasting project.

Data Preparation

Load Data in Fabric

First, you need to put your time series data into Microsoft Fabric. Many people use Yahoo Finance, Alpha Vantage, or Quandl for this. Here is a quick table about these sources:

When you have a lot of data, follow these steps to do better:

1. Keep old data and new updates apart. This helps your work go faster.

2. Use Parquet files in OneLake for better storage and speed.

3. Make a staging area to clean and change your data before moving it.

4. Pick full load to replace all data or incremental load to add only new data.

5. Take care of your tables with regular checks.

6. Watch for changes in your data to help with incremental loading.

You can use T-SQL scripts or Fabric notebooks for these jobs. This setup gets you ready for time series forecasting.

Clean and Explore Data

After you load your data, you must clean and look at it. In Fabric notebooks, you can use Python and PySpark for this. Make sure your date columns use the right datetime format. Fill in missing values, take out repeats, and rename columns to make them clear. Here is a code example you might use:

from pyspark.sql.functions import col, when, lit, to_date

df = spark.sql("SELECT * FROM SalesLakehouse.sales LIMIT 1000")
df = df.withColumn("date", to_date(col("date"), "yyyy-MM-dd HH:mm:ss"))
df = df.withColumn("isPaidTimeOff", when(col("isPaidTimeOff").isNull(), lit(False)).otherwise(col("isPaidTimeOff")))
df_cleaned = df.dropDuplicates()

You can also use Data Wrangler in Fabric notebooks to drop repeats, remove missing values, and drop columns you do not need. After cleaning, look at your data by checking summaries, making box plots, or using Kusto Query Language (KQL) for more study. This step helps you know your data before you build your forecasting models.

Tip: Clean data gives you better forecasts and results you can trust.

Time Series Forecasting in Fabric

Model Selection

You need to choose the right model before you start Time Series Forecasting in Fabric. Most users pick the Prophet model in Fabric notebooks. Prophet works well when your data shows strong seasonal patterns or has many years of history. It can handle missing values and outliers, which makes it a good choice for business data. Prophet uses an additive approach, breaking down your data into trend, seasonality, and holiday effects. You can also use built-in tools to visualize these parts.

To select the best model, follow these steps:

1. Look at your data. Check for trends, seasonality, and any odd spikes.

2. Test if your data is stationary. You can use tests like the Augmented Dickey-Fuller test.

3. Break your data into trend, seasonal, and leftover parts. This helps you see what patterns exist.

4. If your data has clear seasonality and an upward or downward trend, Prophet is a strong choice.

5. For other cases, you might try models like SARIMA, but Prophet is the most supported in Fabric notebooks.

Tip: Clean data and a good understanding of your data’s patterns help you pick the right model for Time Series Forecasting.

Training and Evaluation

After you pick your model, you need to train and test it. Always split your data by time. Use older data for training and newer data for testing. This keeps your results honest and avoids mistakes.

Here is a step-by-step guide:

1. Split your data by date. For example, use the first 80% of your data for training and the last 20% for testing.

2. Train your Prophet model. Try different settings, like changing how sensitive the model is to sudden changes.

3. Validate your model. Use cross-validation to check how well your model predicts future values. Prophet has a built-in function for this.

4. Check your results with metrics. Use numbers like MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error). These tell you how close your forecasts are to real values.

Here is a table of common metrics you can use:

You can use Prophet’s built-in plots to see your forecast, trend, and seasonality. This helps you understand what the model learned.

Note: Use MLflow in Fabric notebooks to track your experiments. Log your models, settings, and results. This helps you compare different runs and pick the best one.

Batch Scoring

Once you have a trained model, you need to make predictions on new data. This is called batch scoring. In Fabric, you can use the PREDICT function to do this at scale.

Here is how you do batch scoring:

• Register your trained model with MLflow in Fabric.

• Load your new data into a Spark DataFrame.

• Use the MLFlowTransformer to apply your model to the new data.

• Save the prediction results back to your lakehouse storage.

You can run batch scoring in different ways. Use notebooks, Spark SQL, or PySpark UDFs. This gives you flexibility based on your workflow.

Batch scoring is fast and works well with large datasets. You can score many time points or series at once. This makes Time Series Forecasting practical for real business needs.

Best Practices:

• Use separate workspaces for different teams.

• Control access to your data and models.

• Back up your work with Git integration.

• Track all your experiments and results in MLflow.

• Use Fabric’s Monitoring Hub to watch your jobs and spot problems early.

By following these steps, you can build, test, and use Time Series Forecasting models in Fabric. You get accurate forecasts and a smooth workflow from start to finish.

Power BI Integration

Import Predictions

You can move your forecast results from Fabric into Power BI. First, save your predictions as a CSV or Parquet file. Open Power BI Desktop and click "Get Data" to find your file. If your data is in OneLake or cloud storage, use the right connector. After loading, check that your date and value columns look right. This helps you make clear and correct reports.

Tip: Use Power Query to clean and fix your data before making charts. This step helps you solve last problems and makes your reports easier to read.

Build Forecast Reports

Now you can make your predictions into fun reports. Start with a line chart. Put your time field on the X-axis and your forecasted values on the Y-axis. Line charts in Power BI are great for showing trends over time. You can add a forecast in the Analytics pane. Change settings like how long the forecast goes, how sure you want to be, and seasonality. The chart will show your forecast as a longer line with shaded areas. This makes it easy to see what might happen next.

• Try the Play Axis custom visual to make your data move over time. This helps you spot patterns.

• Use DAX functions like DATESYTD or SAMEPERIODLASTYEAR for more time study.

• Small Multiple Line Charts let you compare groups side by side.

These tools help you get the most from your Time Series Forecasting results.

Share and Collaborate

Sharing your Power BI reports is easy and gives you choices. You can use different ways to reach your team or others:

You can also set who can see what, use row-level security, and watch how people use your reports. Update your dashboards often to keep them fresh. Teach your users how to use filters and charts so everyone gets the most out of your reports.

---

You now know how to do time series forecasting with Power BI and Fabric. This way lets you change and grow your work as needed. You get autoscaling, pay-as-you-go costs, and easy Azure use. You can set up AutoML or Data Activator to make forecasts happen by themselves. For more learning, try these steps:

1. Make and train models in Power BI or with Fabric AutoML.

2. Look at validation reports and use models on new data.

3. Try tutorials on advanced forecasting in Power BI to learn more.

Keep learning about unified analytics to help your future forecasting projects.

FAQ

How do you update your forecasts when new data arrives?

You can refresh your data in Fabric and Power BI. Set up automatic refresh times. This keeps your forecasts up to date. Always check how well your model works after updates.

Can you use other forecasting models besides Prophet in Fabric?

Yes, you can use models like SARIMA or ARIMA in Fabric notebooks. Install the right Python libraries first. Try different models to find what works best for your data.

What should you do if your data has missing values?

Fill missing values before you train your model. Use easy ways like forward fill or mean value. Clean data helps your forecasts stay correct.

Is it possible to automate the entire forecasting workflow?

You can automate your work using Power BI dataflows, Fabric pipelines, and scheduled notebook runs. This saves time and means you do less by hand.

How do you share your forecast results with people outside your team?

Export your Power BI report as a PDF or share a safe link. Use row-level security to control what others see. You can also publish reports to Microsoft Teams or SharePoint.